diff --git a/.gitignore b/.gitignore index 8931b80..64a7bc3 100644 --- a/.gitignore +++ b/.gitignore @@ -22,6 +22,7 @@ var/ *.egg-info/ .installed.cfg *.egg +.github/ # Virtual environments venv/ diff --git a/POSTMAN_GUIDE.md b/POSTMAN_GUIDE.md new file mode 100644 index 0000000..cebb151 --- /dev/null +++ b/POSTMAN_GUIDE.md @@ -0,0 +1,178 @@ +# 📮 Collection Postman - AI Lab API + +## 📋 Overview + +Cette collection Postman complète contient tous les endpoints de l'API AI Lab avec des exemples prêts à utiliser pour tester chaque pipeline NLP. + +## 📁 Fichiers inclus + +- **`AI_Lab_API.postman_collection.json`** - Collection principale avec tous les endpoints +- **`AI_Lab_API.postman_environment.json`** - Environnement avec variables configurables +- **`POSTMAN_GUIDE.md`** - Ce guide d'utilisation + +## 🚀 Installation et Configuration + +### 1. Importer dans Postman + +1. Ouvrez Postman +2. Cliquez sur **Import** (bouton en haut à gauche) +3. Sélectionnez **Upload Files** +4. Importez les deux fichiers : + - `AI_Lab_API.postman_collection.json` + - `AI_Lab_API.postman_environment.json` + +### 2. Configurer l'environnement + +1. Cliquez sur l'icône **Settings** (⚙️) en haut à droite +2. Sélectionnez **"AI Lab API Environment"** +3. Modifiez `base_url` si nécessaire (par défaut : `http://localhost:8000`) + +### 3. Démarrer l'API + +Avant d'utiliser Postman, assurez-vous que l'API est démarrée : + +```bash +# Dans le dossier du projet +python -m src.main --mode api +# ou +poetry run python src/main.py --mode api --host 0.0.0.0 --port 8000 +``` + +## 📊 Structure de la Collection + +### 🏠 Core Endpoints + +- **Root** - Informations générales de l'API +- **Health Check** - Statut de l'API et pipelines chargés + +### 💭 Sentiment Analysis + +- **Analyze Sentiment - Positive** - Test avec texte positif +- **Analyze Sentiment - Negative** - Test avec texte négatif +- **Analyze Sentiment - Custom Model** - Test avec modèle personnalisé +- **Batch Sentiment Analysis** - Traitement par lot + +### 🏷️ Named Entity Recognition + +- **Extract Entities - People & Organizations** - Entités personnes/organisations +- **Extract Entities - Geographic** - Entités géographiques +- **Batch NER Processing** - Traitement par lot + +### ❓ Question Answering + +- **Simple Q&A** - Questions simples +- **Technical Q&A** - Questions techniques + +### 🎭 Fill Mask + +- **Fill Simple Mask** - Masques simples +- **Fill Technical Mask** - Masques techniques +- **Batch Fill Mask** - Traitement par lot + +### 🛡️ Content Moderation + +- **Check Safe Content** - Contenu sûr +- **Check Potentially Toxic Content** - Contenu potentiellement toxique +- **Batch Content Moderation** - Traitement par lot + +### ✍️ Text Generation + +- **Generate Creative Text** - Génération créative +- **Generate Technical Text** - Génération technique +- **Batch Text Generation** - Traitement par lot + +### 🧪 Testing & Examples + +- **Complete Pipeline Test** - Test complet +- **Error Handling Test - Empty Text** - Gestion d'erreurs (texte vide) +- **Error Handling Test - Invalid Model** - Gestion d'erreurs (modèle invalide) + +## 🔧 Utilisation Avancée + +### Variables d'environnement disponibles + +| Variable | Description | Valeur par défaut | +| ----------------- | ------------------------- | ----------------------- | +| `base_url` | URL de base de l'API | `http://localhost:8000` | +| `api_version` | Version de l'API | `1.0.0` | +| `timeout` | Timeout des requêtes (ms) | `30000` | +| `default_*_model` | Modèles par défaut | Voir environnement | + +### Personnalisation des modèles + +Vous pouvez tester différents modèles en modifiant le champ `model_name` dans le body des requêtes : + +```json +{ + "text": "Your text here", + "model_name": "cardiffnlp/twitter-roberta-base-sentiment-latest" +} +``` + +### Tests automatiques + +Chaque requête inclut des tests automatiques : + +- ✅ Temps de réponse < 30 secondes +- ✅ Header Content-Type présent +- ✅ Logs automatiques dans la console + +## 📈 Exemples d'utilisation + +### 1. Test rapide de l'API + +1. Exécutez **"Health Check"** pour vérifier que l'API fonctionne +2. Testez **"Analyze Sentiment - Positive"** pour un premier test + +### 2. Test complet d'un pipeline + +1. Commencez par un test simple (ex: sentiment positif) +2. Testez avec un modèle personnalisé +3. Testez le traitement par lot +4. Testez la gestion d'erreurs + +### 3. Benchmark de performance + +1. Utilisez **"Batch Text Generation"** avec plusieurs prompts +2. Surveillez les temps de réponse dans l'onglet Tests +3. Ajustez le timeout si nécessaire + +## 🐛 Dépannage + +### API non accessible + +- Vérifiez que l'API est démarrée sur le bon port +- Modifiez `base_url` dans l'environnement si nécessaire + +### Erreurs 422 (Validation Error) + +- Vérifiez le format JSON du body +- Assurez-vous que les champs requis sont présents + +### Erreurs 503 (Service Unavailable) + +- Pipeline non chargé - vérifiez les logs de l'API +- Redémarrez l'API si nécessaire + +### Timeouts + +- Augmentez la valeur `timeout` dans l'environnement +- Certains modèles peuvent être lents au premier chargement + +## 🎯 Bonnes Pratiques + +1. **Démarrez toujours par Health Check** pour vérifier l'état de l'API +2. **Utilisez l'environnement** pour centraliser la configuration +3. **Consultez les logs** dans la console Postman pour déboguer +4. **Testez progressivement** : simple → personnalisé → batch → erreurs +5. **Documentez vos tests** en ajoutant des descriptions aux requêtes + +## 🔗 Liens Utiles + +- **Documentation Swagger** : http://localhost:8000/docs (quand l'API est active) +- **Documentation ReDoc** : http://localhost:8000/redoc +- **Schéma OpenAPI** : http://localhost:8000/openapi.json + +--- + +**Happy Testing! 🚀** diff --git a/README.md b/README.md index 33292e6..55b2e50 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,54 @@ # 🧠 AI Lab – Transformers CLI Playground -> A **pedagogical and technical project** designed for AI practitioners and students to experiment with Hugging Face Transformers through an **interactive Command‑Line Interface (CLI)**. -> This playground provides ready‑to‑use NLP pipelines (Sentiment Analysis, Named Entity Recognition, Text Generation, Fill‑Mask, Moderation, etc.) in a modular, extensible, and educational codebase. +> A **pedagogical and technical project** designed for AI practitioners and students to explore **Hugging Face Transformers** through an **interactive Command-Line Interface (CLI)** or a **REST API**. +> This playground provides ready-to-use NLP pipelines — including **Sentiment Analysis**, **Named Entity Recognition**, **Text Generation**, **Fill-Mask**, **Question Answering (QA)**, **Moderation**, and more — in a modular, extensible, and educational codebase. + +--- + +

+ Python + Poetry + Transformers + License +

+ +--- + +## 📑 Table of Contents + +- [📚 Overview](#-overview) +- [🗂️ Project Structure](#️-project-structure) +- [⚙️ Installation](#️-installation) + - [🧾 Option 1 – Poetry (Recommended)](#-option-1--poetry-recommended) + - [📦 Option 2 – Pip + Requirements](#-option-2--pip--requirements) +- [▶️ Usage](#️-usage) + - [🖥️ CLI Mode](#️-cli-mode) + - [🌐 API Mode](#-api-mode) +- [📡 API Endpoints](#-api-endpoints) +- [🖥️ CLI Examples](#️-cli-examples) +- [🧠 Architecture Overview](#-architecture-overview) +- [⚙️ Configuration](#️-configuration) +- [🧩 Extending the Playground](#-extending-the-playground) +- [🧰 Troubleshooting](#-troubleshooting) +- [🧭 Development Guidelines](#-development-guidelines) +- [🧱 Roadmap](#-roadmap) +- [📜 License](#-license) --- ## 📚 Overview -The **AI Lab – Transformers CLI Playground** allows you to explore multiple natural language processing tasks directly from the terminal. -Each task (e.g., sentiment, NER, text generation) is implemented as a **Command Module**, which interacts with a **Pipeline Module** built on top of the `transformers` library. +The **AI Lab – Transformers CLI Playground** enables users to explore **multiple NLP tasks directly from the terminal or via HTTP APIs**. +Each task (sentiment, NER, text generation, etc.) is implemented as a **Command Module** that communicates with a **Pipeline Module** powered by Hugging Face’s `transformers` library. -The lab is intentionally structured to demonstrate **clean software design for ML codebases** — with strict separation between configuration, pipelines, CLI logic, and display formatting. +The project demonstrates **clean ML code architecture** with strict separation between: + +- Configuration +- Pipelines +- CLI logic +- Display formatting + +It’s a great educational resource for learning **how to structure ML applications** professionally. --- @@ -18,77 +56,74 @@ The lab is intentionally structured to demonstrate **clean software design for M ```text src/ -├── __init__.py ├── main.py # CLI entry point │ ├── cli/ -│ ├── __init__.py -│ ├── base.py # CLICommand base class & interactive shell handler -│ └── display.py # Console formatting utilities (tables, colors, results) +│ ├── base.py # CLICommand base class & interactive shell +│ └── display.py # Console formatting utilities (colors, tables, results) │ ├── commands/ # User-facing commands wrapping pipeline logic -│ ├── __init__.py │ ├── sentiment.py # Sentiment analysis command -│ ├── fillmask.py # Masked token prediction command -│ ├── textgen.py # Text generation command -│ ├── ner.py # Named Entity Recognition command -│ └── moderation.py # Toxicity / content moderation command +│ ├── fillmask.py # Masked token prediction +│ ├── textgen.py # Text generation +│ ├── ner.py # Named Entity Recognition +│ ├── qa.py # Question Answering (extractive) +│ └── moderation.py # Content moderation / toxicity detection │ -├── pipelines/ # Machine learning logic (Hugging Face Transformers) -│ ├── __init__.py +├── pipelines/ # ML logic based on Hugging Face pipelines │ ├── template.py # Blueprint for creating new pipelines │ ├── sentiment.py │ ├── fillmask.py │ ├── textgen.py │ ├── ner.py +│ ├── qa.py │ └── moderation.py │ +├── api/ +│ ├── app.py # FastAPI app and endpoints +│ ├── models.py # Pydantic schemas +│ └── config.py # API configuration +│ └── config/ - ├── __init__.py - └── settings.py # Global configuration (default models, parameters) + └── settings.py # Global configuration (models, params) ``` --- ## ⚙️ Installation -### 🧾 Option 1 – Using Poetry (Recommended) +### 🧾 Option 1 – Poetry (Recommended) -> Poetry is used as the main dependency manager. +> Poetry is the main dependency manager for this project. ```bash -# 1. Create and activate a new virtual environment poetry shell - -# 2. Install dependencies poetry install ``` -This will automatically install all dependencies declared in `pyproject.toml`, including **transformers** and **torch**. +This installs all dependencies defined in `pyproject.toml` (including `transformers`, `torch`, and `fastapi`). -To run the CLI inside the Poetry environment: +Run the app: ```bash -poetry run python src/main.py +# CLI mode +poetry run python src/main.py --mode cli + +# API mode +poetry run python src/main.py --mode api ``` --- -### 📦 Option 2 – Using pip and requirements.txt +### 📦 Option 2 – Pip + requirements.txt -If you prefer using `requirements.txt` manually: +If you prefer manual dependency management: ```bash -# 1. Create a virtual environment python -m venv .venv +source .venv/bin/activate # Linux/macOS +.venv\Scripts\Activate.ps1 # Windows -# 2. Activate it -# Linux/macOS -source .venv/bin/activate -# Windows PowerShell -.venv\Scripts\Activate.ps1 - -# 3. Install dependencies pip install -r requirements.txt ``` @@ -96,15 +131,15 @@ pip install -r requirements.txt ## ▶️ Usage -Once installed, launch the CLI with: +### 🖥️ CLI Mode + +Run the interactive CLI: ```bash -python -m src.main -# or, if using Poetry -poetry run python src/main.py +python -m src.main --mode cli ``` -You’ll see an interactive menu listing the available commands: +Interactive menu: ``` Welcome to AI Lab - Transformers CLI Playground @@ -113,36 +148,89 @@ Available commands: • fillmask – Predict masked words in a sentence • textgen – Generate text from a prompt • ner – Extract named entities from text + • qa – Answer questions from a context • moderation – Detect toxic or unsafe content ``` -### Example Sessions +--- -#### 🔹 Sentiment Analysis +### 🌐 API Mode + +Run FastAPI server: + +```bash +python -m src.main --mode api +# Custom config +python -m src.main --mode api --host 0.0.0.0 --port 8000 --reload +``` + +API Docs: + +- **Swagger** → http://localhost:8000/docs +- **ReDoc** → http://localhost:8000/redoc +- **OpenAPI** → http://localhost:8000/openapi.json + +--- + +## 📡 API Endpoints + +### Core Endpoints + +| Method | Endpoint | Description | +| ------ | --------- | ------------------------- | +| `GET` | `/` | Health check and API info | +| `GET` | `/health` | Detailed health status | + +### Individual Processing + +| Method | Endpoint | Description | +| ------ | ------------- | ---------------------- | +| `POST` | `/sentiment` | Analyze text sentiment | +| `POST` | `/fillmask` | Predict masked words | +| `POST` | `/textgen` | Generate text | +| `POST` | `/ner` | Extract named entities | +| `POST` | `/qa` | Question answering | +| `POST` | `/moderation` | Content moderation | + +### Batch Processing + +| Method | Endpoint | Description | +| ------ | ------------------- | -------------------------- | +| `POST` | `/sentiment/batch` | Process multiple texts | +| `POST` | `/fillmask/batch` | Fill multiple masked texts | +| `POST` | `/textgen/batch` | Generate from prompts | +| `POST` | `/ner/batch` | Extract entities in batch | +| `POST` | `/qa/batch` | Answer questions in batch | +| `POST` | `/moderation/batch` | Moderate multiple texts | + +--- + +## 🖥️ CLI Examples + +### 🔹 Sentiment Analysis ```text 💬 Enter text: I absolutely love this project! → Sentiment: POSITIVE (score: 0.998) ``` -#### 🔹 Fill‑Mask +### 🔹 Fill-Mask ```text 💬 Enter text: The capital of France is [MASK]. → Predictions: 1) Paris score: 0.87 2) Lyon score: 0.04 - 3) London score: 0.02 ``` -#### 🔹 Text Generation +### 🔹 Text Generation ```text 💬 Prompt: Once upon a time → Output: Once upon a time there was a young AI learning to code... ``` -#### 🔹 NER (Named Entity Recognition) +### 🔹 NER ```text 💬 Enter text: Elon Musk founded SpaceX in California. @@ -152,7 +240,15 @@ Available commands: - California (LOC) ``` -#### 🔹 Moderation +### 🔹 QA (Question Answering) + +```text +💬 Enter question: What is the capital of France? +💬 Enter context: France is a country in Europe. Its capital is Paris. +→ Answer: The capital of France is Paris. +``` + +### 🔹 Moderation ```text 💬 Enter text: I hate everything! @@ -163,50 +259,34 @@ Available commands: ## 🧠 Architecture Overview -The internal structure follows a clean **Command ↔ Pipeline ↔ Display** pattern: +Both CLI and API share the **same pipeline layer**, ensuring code reusability and consistency. + +### CLI Architecture ```text - ┌──────────────────────┐ - │ InteractiveCLI │ - │ (src/cli/base.py) │ - └──────────┬───────────┘ - │ - ▼ - ┌─────────────────┐ - │ Command Layer │ ← e.g. sentiment.py - │ (user commands) │ - └───────┬─────────┘ - │ - ▼ - ┌─────────────────┐ - │ Pipeline Layer │ ← e.g. pipelines/sentiment.py - │ (ML logic) │ - └───────┬─────────┘ - │ - ▼ - ┌─────────────────┐ - │ Display Layer │ ← cli/display.py - │ (format output) │ - └─────────────────┘ +InteractiveCLI → Command Layer → Pipeline Layer → Display Layer ``` -### Key Concepts +### API Architecture -| Layer | Description | -| ------------ | -------------------------------------------------------------------------- | -| **CLI** | Manages user input/output, help menus, and navigation between commands. | -| **Command** | Encapsulates a single user-facing operation (e.g., run sentiment). | -| **Pipeline** | Wraps Hugging Face’s `transformers.pipeline()` to perform inference. | -| **Display** | Handles clean console rendering (colored output, tables, JSON formatting). | -| **Config** | Centralizes model names, limits, and global constants. | +```text +FastAPI App → Pydantic Models → Pipeline Layer → JSON Response +``` + +| Layer | Description | +| ------------ | ---------------------------------------------- | +| **CLI** | Manages user input/output and navigation. | +| **API** | Exposes endpoints with automatic OpenAPI docs. | +| **Command** | Encapsulates user-facing operations. | +| **Pipeline** | Wraps Hugging Face’s pipelines. | +| **Models** | Validates requests/responses. | +| **Display** | Formats console output. | --- ## ⚙️ Configuration -All configuration is centralized in `src/config/settings.py`. - -Example: +All configuration is centralized in `src/config/settings.py`: ```python class Config: @@ -215,88 +295,75 @@ class Config: "fillmask": "bert-base-uncased", "textgen": "gpt2", "ner": "dslim/bert-base-NER", - "moderation":"unitary/toxic-bert" + "qa": "distilbert-base-cased-distilled-squad", + "moderation":"unitary/toxic-bert", } MAX_LENGTH = 512 BATCH_SIZE = 8 ``` -You can easily modify model names to experiment with different checkpoints. - --- ## 🧩 Extending the Playground -To create a new experiment (e.g., keyword extraction): +To add a new NLP experiment (e.g., keyword extraction): -1. **Duplicate** `src/pipelines/template.py` → `src/pipelines/keywords.py` - Implement the `run()` or `analyze()` logic using a new Hugging Face pipeline. +1. Duplicate `src/pipelines/template.py` → `src/pipelines/keywords.py` +2. Create a command: `src/commands/keywords.py` +3. Register it in `src/main.py` +4. Add Pydantic models and API endpoint +5. Update `Config.DEFAULT_MODELS` -2. **Create a Command** in `src/commands/keywords.py` to interact with users. - -3. **Register the command** inside `src/main.py`: - -```python -from src.commands.keywords import KeywordsCommand -cli.register_command(KeywordsCommand()) -``` - -4. Optionally, add a model name in `Config.DEFAULT_MODELS`. - ---- - -## 🧪 Testing - -You can use `pytest` for lightweight validation: - -```bash -pip install pytest -pytest -q -``` - -Recommended structure: - -``` -tests/ -├── test_sentiment.py -├── test_textgen.py -└── ... -``` +Both CLI and API will automatically share this logic. --- ## 🧰 Troubleshooting -| Issue | Cause / Solution | -| ---------------------------- | -------------------------------------------- | -| **`transformers` not found** | Check virtual environment activation. | -| **Torch fails to install** | Install CPU-only version from PyTorch index. | -| **Models download slowly** | Hugging Face caches them after first run. | -| **Unicode / accents broken** | Ensure terminal encoding is UTF‑8. | +| Issue | Solution | +| ------------------------ | ----------------------- | +| `transformers` not found | Activate your venv. | +| Torch install fails | Use CPU-only wheel. | +| Models download slowly | Cached after first use. | +| Encoding issues | Ensure UTF-8 terminal. | + +### API Issues + +| Issue | Solution | +| -------------------- | --------------------------------------- | +| `FastAPI` missing | `pip install fastapi uvicorn[standard]` | +| Port in use | Change with `--port 8001` | +| CORS error | Edit `allow_origins` in `api/config.py` | +| Validation error 422 | Check request body | +| 500 error | Verify model loading | --- ## 🧭 Development Guidelines -- Keep **Command** classes lightweight — no ML logic inside them. -- Reuse the **Pipeline Template** for new experiments. -- Format outputs consistently via the `DisplayFormatter`. -- Document all new models or commands in `README.md` and `settings.py`. +- Keep command classes lightweight (no ML inside) +- Use the pipeline template for new tasks +- Format all outputs via `DisplayFormatter` +- Document new commands and models --- ## 🧱 Roadmap -- [ ] Add non-interactive CLI flags (`--text`, `--task`) -- [ ] Add multilingual model options -- [ ] Add automatic test coverage -- [ ] Add logging and profiling utilities -- [ ] Add export to JSON/CSV results +- [ ] Non-interactive CLI flags (`--text`, `--task`) +- [ ] Multilingual models +- [ ] Test coverage +- [ ] Logging & profiling +- [ ] Export to JSON/CSV --- ## 📜 License -This project is licensed under the [MIT License](./LICENSE) — feel free to use it, modify it, and share it! +Licensed under the [MIT License](./LICENSE). +You are free to use, modify, and distribute this project. --- + +✨ **End of Documentation** +_The AI Lab – Transformers CLI Playground: built for learning, experimenting, and sharing NLP excellence._ diff --git a/demo_api.sh b/demo_api.sh new file mode 100755 index 0000000..ba4b817 --- /dev/null +++ b/demo_api.sh @@ -0,0 +1,68 @@ +#!/bin/bash + +echo "🚀 AI Lab API Demo" +echo "==================" +echo "" + +# Configuration +API_BASE="http://127.0.0.1:8000" + +echo "📊 Health Check:" +curl -s -X GET "$API_BASE/health" | python3 -m json.tool +echo "" + +echo "😊 Sentiment Analysis:" +curl -s -X POST "$API_BASE/sentiment" \ + -H "Content-Type: application/json" \ + -d '{"text": "This API is absolutely amazing! I love it!"}' | python3 -m json.tool +echo "" + +echo "🔍 Named Entity Recognition:" +curl -s -X POST "$API_BASE/ner" \ + -H "Content-Type: application/json" \ + -d '{"text": "Apple Inc. was founded by Steve Jobs in Cupertino, California."}' | python3 -m json.tool +echo "" + +echo "❓ Question Answering:" +curl -s -X POST "$API_BASE/qa" \ + -H "Content-Type: application/json" \ + -d '{ + "question": "Who founded Apple?", + "context": "Apple Inc. was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in April 1976." + }' | python3 -m json.tool +echo "" + +echo "� Fill Mask:" +curl -s -X POST "$API_BASE/fillmask" \ + -H "Content-Type: application/json" \ + -d '{"text": "The capital of France is [MASK]."}' | python3 -m json.tool +echo "" + +echo "🛡️ Content Moderation:" +curl -s -X POST "$API_BASE/moderation" \ + -H "Content-Type: application/json" \ + -d '{"text": "This is a completely normal and safe text."}' | python3 -m json.tool +echo "" + +echo "✍️ Text Generation:" +curl -s -X POST "$API_BASE/textgen" \ + -H "Content-Type: application/json" \ + -d '{"text": "Once upon a time, in a distant galaxy"}' | python3 -m json.tool +echo "" + +echo "📦 Batch Sentiment Analysis:" +curl -s -X POST "$API_BASE/sentiment/batch" \ + -H "Content-Type: application/json" \ + -d '{ + "texts": [ + "I love this!", + "This is terrible.", + "Neutral statement here." + ] + }' | python3 -m json.tool +echo "" + +echo "�🏥 Final Health Check:" +curl -s -X GET "$API_BASE/health" | python3 -m json.tool +echo "" +echo "✅ Demo completed! Check the API documentation at: $API_BASE/docs" \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index b7304dc..5014372 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,36 +2,65 @@ [[package]] name = "accelerate" -version = "1.10.1" +version = "0.20.3" description = "Accelerate" optional = false -python-versions = ">=3.9.0" +python-versions = ">=3.7.0" groups = ["main"] files = [ - {file = "accelerate-1.10.1-py3-none-any.whl", hash = "sha256:3621cff60b9a27ce798857ece05e2b9f56fcc71631cfb31ccf71f0359c311f11"}, - {file = "accelerate-1.10.1.tar.gz", hash = "sha256:3dea89e433420e4bfac0369cae7e36dcd6a56adfcfd38cdda145c6225eab5df8"}, + {file = "accelerate-0.20.3-py3-none-any.whl", hash = "sha256:147183e7a2215f7bd45a7af3b986a963daa8a61fa58b0912b9473049e011ad15"}, + {file = "accelerate-0.20.3.tar.gz", hash = "sha256:79a896978c20dac270083d42bf033f4c9a80dcdd6b946f1ca92d8d6d0f0f5ba9"}, ] [package.dependencies] -huggingface_hub = ">=0.21.0" -numpy = ">=1.17,<3.0.0" +numpy = 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[ + { + "name": "Root - API Information", + "request": { + "method": "GET", + "header": [], + "url": { + "raw": "{{base_url}}/", + "host": ["{{base_url}}"], + "path": [""] + }, + "description": "Get API information and available endpoints" + }, + "response": [] + }, + { + "name": "Health Check", + "request": { + "method": "GET", + "header": [], + "url": { + "raw": "{{base_url}}/health", + "host": ["{{base_url}}"], + "path": ["health"] + }, + "description": "Check API health status and loaded pipelines" + }, + "response": [] + } + ], + "description": "Core API endpoints for health check and information" + }, + { + "name": "💭 Sentiment Analysis", + "item": [ + { + "name": "Analyze Sentiment - Positive", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"I absolutely love this project! It's amazing and well-designed.\"\n}" + }, + "url": { + "raw": "{{base_url}}/sentiment", + "host": ["{{base_url}}"], + "path": ["sentiment"] + }, + "description": "Analyze sentiment of positive text" + }, + "response": [] + }, + { + "name": "Analyze Sentiment - Negative", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"This is terrible and I hate it completely.\"\n}" + }, + "url": { + "raw": "{{base_url}}/sentiment", + "host": ["{{base_url}}"], + "path": ["sentiment"] + }, + "description": "Analyze sentiment of negative text" + }, + "response": [] + }, + { + "name": "Analyze Sentiment - Custom Model", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"This product is okay, nothing special.\",\n \"model_name\": \"cardiffnlp/twitter-roberta-base-sentiment-latest\"\n}" + }, + "url": { + "raw": "{{base_url}}/sentiment", + "host": ["{{base_url}}"], + "path": ["sentiment"] + }, + "description": "Analyze sentiment using custom model" + }, + "response": [] + }, + { + "name": "Batch Sentiment Analysis", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"texts\": [\n \"I love this!\",\n \"This is terrible.\",\n \"It's okay, nothing special.\",\n \"Amazing product, highly recommended!\",\n \"Worst experience ever.\"\n ]\n}" + }, + "url": { + "raw": "{{base_url}}/sentiment/batch", + "host": ["{{base_url}}"], + "path": ["sentiment", "batch"] + }, + "description": "Analyze sentiment for multiple texts" + }, + "response": [] + } + ], + "description": "Sentiment analysis endpoints for analyzing emotional tone" + }, + { + "name": "🏷️ Named Entity Recognition", + "item": [ + { + "name": "Extract Entities - People & Organizations", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"Elon Musk is the CEO of Tesla and SpaceX. He was born in South Africa and now lives in California.\"\n}" + }, + "url": { + "raw": "{{base_url}}/ner", + "host": ["{{base_url}}"], + "path": ["ner"] + }, + "description": "Extract named entities from text" + }, + "response": [] + }, + { + "name": "Extract Entities - Geographic", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"The meeting will be held in Paris, France on Monday. We'll then travel to London, United Kingdom.\"\n}" + }, + "url": { + "raw": "{{base_url}}/ner", + "host": ["{{base_url}}"], + "path": ["ner"] + }, + "description": "Extract geographic entities" + }, + "response": [] + }, + { + "name": "Batch NER Processing", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"texts\": [\n \"Apple Inc. is headquartered in Cupertino, California.\",\n \"Microsoft was founded by Bill Gates and Paul Allen.\",\n \"The conference will be in Tokyo, Japan next month.\"\n ]\n}" + }, + "url": { + "raw": "{{base_url}}/ner/batch", + "host": ["{{base_url}}"], + "path": ["ner", "batch"] + }, + "description": "Extract entities from multiple texts" + }, + "response": [] + } + ], + "description": "Named Entity Recognition endpoints for extracting people, places, organizations" + }, + { + "name": "❓ Question Answering", + "item": [ + { + "name": "Simple Q&A", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"question\": \"What is the capital of France?\",\n \"context\": \"France is a country in Europe. Paris is the capital and largest city of France. The city is known for the Eiffel Tower and the Louvre Museum.\"\n}" + }, + "url": { + "raw": "{{base_url}}/qa", + "host": ["{{base_url}}"], + "path": ["qa"] + }, + "description": "Answer questions based on context" + }, + "response": [] + }, + { + "name": "Technical Q&A", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"question\": \"What programming language is mentioned?\",\n \"context\": \"FastAPI is a modern, fast web framework for building APIs with Python 3.7+. It provides automatic interactive API documentation and is built on top of Starlette and Pydantic.\"\n}" + }, + "url": { + "raw": "{{base_url}}/qa", + "host": ["{{base_url}}"], + "path": ["qa"] + }, + "description": "Answer technical questions" + }, + "response": [] + } + ], + "description": "Question Answering endpoints for extracting answers from context" + }, + { + "name": "🎭 Fill Mask", + "item": [ + { + "name": "Fill Simple Mask", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"The capital of France is [MASK].\"\n}" + }, + "url": { + "raw": "{{base_url}}/fillmask", + "host": ["{{base_url}}"], + "path": ["fillmask"] + }, + "description": "Predict masked words in sentences" + }, + "response": [] + }, + { + "name": "Fill Technical Mask", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"Python is a popular [MASK] language for machine learning.\"\n}" + }, + "url": { + "raw": "{{base_url}}/fillmask", + "host": ["{{base_url}}"], + "path": ["fillmask"] + }, + "description": "Fill technical context masks" + }, + "response": [] + }, + { + "name": "Batch Fill Mask", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"texts\": [\n \"The weather today is [MASK].\",\n \"I like to eat [MASK] for breakfast.\",\n \"The best programming language is [MASK].\"\n ]\n}" + }, + "url": { + "raw": "{{base_url}}/fillmask/batch", + "host": ["{{base_url}}"], + "path": ["fillmask", "batch"] + }, + "description": "Fill masks in multiple texts" + }, + "response": [] + } + ], + "description": "Fill Mask endpoints for predicting masked words" + }, + { + "name": "🛡️ Content Moderation", + "item": [ + { + "name": "Check Safe Content", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"This is a wonderful day and I'm feeling great!\"\n}" + }, + "url": { + "raw": "{{base_url}}/moderation", + "host": ["{{base_url}}"], + "path": ["moderation"] + }, + "description": "Check safe, non-toxic content" + }, + "response": [] + }, + { + "name": "Check Potentially Toxic Content", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"I hate everything and everyone around me!\"\n}" + }, + "url": { + "raw": "{{base_url}}/moderation", + "host": ["{{base_url}}"], + "path": ["moderation"] + }, + "description": "Check potentially toxic content" + }, + "response": [] + }, + { + "name": "Batch Content Moderation", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"texts\": [\n \"Have a great day!\",\n \"I'm so angry right now!\",\n \"Thank you for your help.\",\n \"This is completely stupid!\"\n ]\n}" + }, + "url": { + "raw": "{{base_url}}/moderation/batch", + "host": ["{{base_url}}"], + "path": ["moderation", "batch"] + }, + "description": "Moderate multiple texts for toxicity" + }, + "response": [] + } + ], + "description": "Content Moderation endpoints for detecting toxic or harmful content" + }, + { + "name": "✍️ Text Generation", + "item": [ + { + "name": "Generate Creative Text", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"Once upon a time in a magical forest\"\n}" + }, + "url": { + "raw": "{{base_url}}/textgen", + "host": ["{{base_url}}"], + "path": ["textgen"] + }, + "description": "Generate creative text from prompt" + }, + "response": [] + }, + { + "name": "Generate Technical Text", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"FastAPI is a modern Python web framework that\"\n}" + }, + "url": { + "raw": "{{base_url}}/textgen", + "host": ["{{base_url}}"], + "path": ["textgen"] + }, + "description": "Generate technical documentation text" + }, + "response": [] + }, + { + "name": "Batch Text Generation", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"texts\": [\n \"In the future, AI will\",\n \"The best way to learn programming is\",\n \"Climate change is\"\n ]\n}" + }, + "url": { + "raw": "{{base_url}}/textgen/batch", + "host": ["{{base_url}}"], + "path": ["textgen", "batch"] + }, + "description": "Generate text from multiple prompts" + }, + "response": [] + } + ], + "description": "Text Generation endpoints for creating text from prompts" + }, + { + "name": "🧪 Testing & Examples", + "item": [ + { + "name": "Complete Pipeline Test", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"AI Lab is an amazing project for learning NLP!\"\n}" + }, + "url": { + "raw": "{{base_url}}/sentiment", + "host": ["{{base_url}}"], + "path": ["sentiment"] + }, + "description": "Test with project-related text" + }, + "response": [] + }, + { + "name": "Error Handling Test - Empty Text", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"\"\n}" + }, + "url": { + "raw": "{{base_url}}/sentiment", + "host": ["{{base_url}}"], + "path": ["sentiment"] + }, + "description": "Test error handling with empty text" + }, + "response": [] + }, + { + "name": "Error Handling Test - Invalid Model", + "request": { + "method": "POST", + "header": [ + { + "key": "Content-Type", + "value": "application/json" + } + ], + "body": { + "mode": "raw", + "raw": "{\n \"text\": \"Test text\",\n \"model_name\": \"non-existent-model\"\n}" + }, + "url": { + "raw": "{{base_url}}/sentiment", + "host": ["{{base_url}}"], + "path": ["sentiment"] + }, + "description": "Test error handling with invalid model" + }, + "response": [] + } + ], + "description": "Testing endpoints and error handling examples" + } + ], + "event": [ + { + "listen": "prerequest", + "script": { + "type": "text/javascript", + "exec": [ + "// Pre-request script for all requests", + "console.log('Making request to: ' + pm.request.url);", + "", + "// Add timestamp to request", + "pm.globals.set('request_timestamp', new Date().toISOString());" + ] + } + }, + { + "listen": "test", + "script": { + "type": "text/javascript", + "exec": [ + "// Common tests for all requests", + "pm.test('Response time is less than 30 seconds', function () {", + " pm.expect(pm.response.responseTime).to.be.below(30000);", + "});", + "", + "pm.test('Response has Content-Type header', function () {", + " pm.expect(pm.response.headers.get('Content-Type')).to.include('application/json');", + "});", + "", + "// Log response for debugging", + "console.log('Response status:', pm.response.status);", + "console.log('Response time:', pm.response.responseTime + 'ms');" + ] + } + } + ] +} diff --git a/postman/AI_Lab_API.postman_environment.json b/postman/AI_Lab_API.postman_environment.json new file mode 100644 index 0000000..b270a0e --- /dev/null +++ b/postman/AI_Lab_API.postman_environment.json @@ -0,0 +1,63 @@ +{ + "id": "ai-lab-api-environment", + "name": "AI Lab API Environment", + "values": [ + { + "key": "base_url", + "value": "http://localhost:8000", + "type": "default", + "description": "Base URL for AI Lab API (default: localhost)" + }, + { + "key": "api_version", + "value": "1.0.0", + "type": "default", + "description": "API version" + }, + { + "key": "timeout", + "value": "30000", + "type": "default", + "description": "Request timeout in milliseconds" + }, + { + "key": "default_sentiment_model", + "value": "distilbert-base-uncased-finetuned-sst-2-english", + "type": "default", + "description": "Default sentiment analysis model" + }, + { + "key": "default_ner_model", + "value": "dslim/bert-base-NER", + "type": "default", + "description": "Default NER model" + }, + { + "key": "default_qa_model", + "value": "distilbert-base-cased-distilled-squad", + "type": "default", + "description": "Default Q&A model" + }, + { + "key": "default_fillmask_model", + "value": "bert-base-uncased", + "type": "default", + "description": "Default fill mask model" + }, + { + "key": "default_textgen_model", + "value": "gpt2", + "type": "default", + "description": "Default text generation model" + }, + { + "key": "default_moderation_model", + "value": "unitary/toxic-bert", + "type": "default", + "description": "Default content moderation model" + } + ], + "_postman_variable_scope": "environment", + "_postman_exported_at": "2024-10-12T10:00:00.000Z", + "_postman_exported_using": "Postman/10.18.0" +} diff --git a/pyproject.toml b/pyproject.toml index 20222d1..ab2496d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,6 +18,9 @@ transformers = "^4.30.0" tokenizers = "^0.13.0" numpy = "^1.24.0" accelerate = "^0.20.0" +fastapi = "^0.104.0" +uvicorn = { extras = ["standard"], version = "^0.24.0" } +pydantic = "^2.5.0" [tool.poetry.scripts] ai-lab = "src.main:main" diff --git a/src/api/__init__.py b/src/api/__init__.py new file mode 100644 index 0000000..e3c391c --- /dev/null +++ b/src/api/__init__.py @@ -0,0 +1,7 @@ +""" +API module for AI Lab +""" +from .models import * +from .app import app + +__all__ = ["app"] \ No newline at end of file diff --git a/src/api/app.py b/src/api/app.py new file mode 100644 index 0000000..dded0e0 --- /dev/null +++ b/src/api/app.py @@ -0,0 +1,494 @@ +""" +FastAPI application for AI Lab +""" +from fastapi import FastAPI, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from contextlib import asynccontextmanager +from typing import Dict, Any +import logging + +from .models import ( + TextRequest, TextListRequest, QARequest, FillMaskRequest, + SentimentResponse, NERResponse, QAResponse, FillMaskResponse, + ModerationResponse, TextGenResponse, BatchResponse +) + +# Global pipeline instances +pipelines: Dict[str, Any] = {} + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Manage application lifespan - load models on startup""" + global pipelines + + # Load all pipelines on startup + try: + logging.info("Loading AI pipelines...") + + # Import here to avoid circular imports + from src.pipelines.sentiment import SentimentAnalyzer + from src.pipelines.ner import NamedEntityRecognizer + from src.pipelines.qa import QuestionAnsweringSystem + from src.pipelines.fillmask import FillMaskAnalyzer + from src.pipelines.moderation import ContentModerator + from src.pipelines.textgen import TextGenerator + + pipelines["sentiment"] = SentimentAnalyzer() + pipelines["ner"] = NamedEntityRecognizer() + pipelines["qa"] = QuestionAnsweringSystem() + pipelines["fillmask"] = FillMaskAnalyzer() + pipelines["moderation"] = ContentModerator() + pipelines["textgen"] = TextGenerator() + logging.info("All pipelines loaded successfully!") + except Exception as e: + logging.error(f"Error loading pipelines: {e}") + # Don't raise, just log - allows API to start without all pipelines + + yield + + # Cleanup on shutdown + pipelines.clear() + logging.info("Pipelines cleaned up") + + +# Create FastAPI app +app = FastAPI( + title="AI Lab API", + description="API for various AI/ML pipelines using transformers", + version="1.0.0", + lifespan=lifespan, + swagger_ui_parameters={ + "syntaxHighlight.theme": "obsidian", + "tryItOutEnabled": True, + "requestSnippetsEnabled": True, + "persistAuthorization": True, + "displayRequestDuration": True, + "defaultModelRendering": "model" + } +) + +# Add CORS middleware +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # Configure appropriately for production + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +@app.get("/") +async def root(): + """Root endpoint""" + return { + "message": "Welcome to AI Lab API", + "version": "1.0.0", + "available_endpoints": [ + "/sentiment", + "/ner", + "/qa", + "/fillmask", + "/moderation", + "/textgen", + "/sentiment/batch", + "/ner/batch", + "/fillmask/batch", + "/moderation/batch", + "/textgen/batch", + "/health", + "/docs" + ] + } + + +@app.get("/health") +async def health_check(): + """Health check endpoint""" + return { + "status": "healthy", + "pipelines_loaded": len(pipelines), + "available_pipelines": list(pipelines.keys()) + } + + +@app.post("/sentiment", response_model=SentimentResponse) +async def analyze_sentiment(request: TextRequest): + """Analyze sentiment of a text""" + try: + if "sentiment" not in pipelines: + raise HTTPException(status_code=503, detail="Sentiment pipeline not available") + + # Use custom model if provided + if request.model_name: + from src.pipelines.sentiment import SentimentAnalyzer + analyzer = SentimentAnalyzer(request.model_name) + result = analyzer.analyze(request.text) + else: + result = pipelines["sentiment"].analyze(request.text) + + if "error" in result: + return SentimentResponse(success=False, text=request.text, message=result["error"]) + + return SentimentResponse( + success=True, + text=result["text"], + sentiment=result["sentiment"], + confidence=result["confidence"] + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/ner", response_model=NERResponse) +async def extract_entities(request: TextRequest): + """Extract named entities from text""" + try: + logging.info(f"NER request for text: {request.text[:50]}...") + if "ner" not in pipelines: + raise HTTPException(status_code=503, detail="NER pipeline not available") + + try: + if request.model_name: + from src.pipelines.ner import NamedEntityRecognizer + ner = NamedEntityRecognizer(request.model_name) + result = ner.recognize(request.text) + else: + result = pipelines["ner"].recognize(request.text) + except Exception as pipeline_error: + logging.error(f"Pipeline error: {str(pipeline_error)}") + return NERResponse(success=False, text=request.text, message=f"Pipeline error: {str(pipeline_error)}") + + logging.info(f"NER result keys: {list(result.keys())}") + + if "error" in result: + logging.error(f"NER error: {result['error']}") + return NERResponse(success=False, text=request.text, message=result["error"]) + + # Validate result structure + if "original_text" not in result: + logging.error(f"Missing 'original_text' in result: {result}") + return NERResponse(success=False, text=request.text, message="Invalid NER result format") + + if "entities" not in result: + logging.error(f"Missing 'entities' in result: {result}") + return NERResponse(success=False, text=request.text, message="Invalid NER result format") + + return NERResponse( + success=True, + text=result["original_text"], + entities=result["entities"] + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/qa", response_model=QAResponse) +async def answer_question(request: QARequest): + """Answer a question based on context""" + try: + if "qa" not in pipelines: + raise HTTPException(status_code=503, detail="QA pipeline not available") + + if request.model_name: + from src.pipelines.qa import QuestionAnsweringSystem + qa = QuestionAnsweringSystem(request.model_name) + result = qa.answer(request.question, request.context) + else: + result = pipelines["qa"].answer(request.question, request.context) + + if "error" in result: + return QAResponse( + success=False, + question=request.question, + context=request.context, + message=result["error"] + ) + + return QAResponse( + success=True, + question=result["question"], + context=result["context"], + answer=result["answer"], + confidence=result["confidence"] + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/fillmask", response_model=FillMaskResponse) +async def fill_mask(request: FillMaskRequest): + """Fill masked words in text""" + try: + if "fillmask" not in pipelines: + raise HTTPException(status_code=503, detail="Fill-mask pipeline not available") + + if request.model_name: + from src.pipelines.fillmask import FillMaskAnalyzer + fillmask = FillMaskAnalyzer(request.model_name) + result = fillmask.predict(request.text) + else: + result = pipelines["fillmask"].predict(request.text) + + if "error" in result: + return FillMaskResponse(success=False, text=request.text, message=result["error"]) + + return FillMaskResponse( + success=True, + text=result["original_text"], + predictions=result["predictions"] + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/moderation", response_model=ModerationResponse) +async def moderate_content(request: TextRequest): + """Moderate content for inappropriate material""" + try: + if "moderation" not in pipelines: + raise HTTPException(status_code=503, detail="Moderation pipeline not available") + + if request.model_name: + from src.pipelines.moderation import ContentModerator + moderation = ContentModerator(request.model_name) + result = moderation.moderate(request.text) + else: + result = pipelines["moderation"].moderate(request.text) + + if "error" in result: + return ModerationResponse(success=False, text=request.text, message=result["error"]) + + # Map the result fields correctly + flagged = result.get("is_modified", False) or result.get("toxic_score", 0.0) > 0.5 + categories = { + "toxic_score": result.get("toxic_score", 0.0), + "is_modified": result.get("is_modified", False), + "restored_text": result.get("moderated_text", request.text), + "words_replaced": result.get("words_replaced", 0) + } + + return ModerationResponse( + success=True, + text=result["original_text"], + flagged=flagged, + categories=categories + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/textgen", response_model=TextGenResponse) +async def generate_text(request: TextRequest): + """Generate text from a prompt""" + try: + if "textgen" not in pipelines: + raise HTTPException(status_code=503, detail="Text generation pipeline not available") + + logging.info(f"Generating text for prompt: {request.text[:50]}...") + + if request.model_name: + from src.pipelines.textgen import TextGenerator + textgen = TextGenerator(request.model_name) + result = textgen.generate(request.text) + else: + result = pipelines["textgen"].generate(request.text) + + logging.info(f"Generation result keys: {list(result.keys())}") + + if "error" in result: + logging.error(f"Generation error: {result['error']}") + return TextGenResponse(success=False, prompt=request.text, message=result["error"]) + + # Extract the generated text from the first generation + generated_text = "" + if "generations" in result and len(result["generations"]) > 0: + # Get the continuation (text after the prompt) from the first generation + generated_text = result["generations"][0].get("continuation", "") + logging.info(f"Extracted generated text: {generated_text[:100]}...") + else: + logging.warning("No generations found in result") + + return TextGenResponse( + success=True, + prompt=result["prompt"], + generated_text=result["prompt"] + " " + generated_text + ) + except Exception as e: + logging.error(f"TextGen endpoint error: {str(e)}", exc_info=True) + raise HTTPException(status_code=500, detail=str(e)) + + +# Batch processing endpoints +@app.post("/sentiment/batch", response_model=BatchResponse) +async def analyze_sentiment_batch(request: TextListRequest): + """Analyze sentiment for multiple texts""" + try: + if "sentiment" not in pipelines: + raise HTTPException(status_code=503, detail="Sentiment pipeline not available") + + analyzer = pipelines["sentiment"] + if request.model_name: + from src.pipelines.sentiment import SentimentAnalyzer + analyzer = SentimentAnalyzer(request.model_name) + + results = [] + failed_count = 0 + + for text in request.texts: + try: + result = analyzer.analyze(text) + if "error" in result: + failed_count += 1 + results.append(result) + except Exception as e: + failed_count += 1 + results.append({"text": text, "error": str(e)}) + + return BatchResponse( + success=True, + results=results, + processed_count=len(request.texts), + failed_count=failed_count + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/ner/batch", response_model=BatchResponse) +async def extract_entities_batch(request: TextListRequest): + """Extract entities from multiple texts""" + try: + if "ner" not in pipelines: + raise HTTPException(status_code=503, detail="NER pipeline not available") + + ner = pipelines["ner"] + if request.model_name: + from src.pipelines.ner import NamedEntityRecognizer + ner = NamedEntityRecognizer(request.model_name) + + results = [] + failed_count = 0 + + for text in request.texts: + try: + result = ner.recognize(text) + if "error" in result: + failed_count += 1 + results.append(result) + except Exception as e: + failed_count += 1 + results.append({"text": text, "error": str(e)}) + + return BatchResponse( + success=True, + results=results, + processed_count=len(request.texts), + failed_count=failed_count + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/fillmask/batch", response_model=BatchResponse) +async def fill_mask_batch(request: TextListRequest): + """Fill masked words in multiple texts""" + try: + if "fillmask" not in pipelines: + raise HTTPException(status_code=503, detail="Fill-mask pipeline not available") + + fillmask = pipelines["fillmask"] + if request.model_name: + from src.pipelines.fillmask import FillMaskAnalyzer + fillmask = FillMaskAnalyzer(request.model_name) + + results = [] + failed_count = 0 + + for text in request.texts: + try: + result = fillmask.predict(text) + if "error" in result: + failed_count += 1 + results.append(result) + except Exception as e: + failed_count += 1 + results.append({"text": text, "error": str(e)}) + + return BatchResponse( + success=True, + results=results, + processed_count=len(request.texts), + failed_count=failed_count + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/moderation/batch", response_model=BatchResponse) +async def moderate_content_batch(request: TextListRequest): + """Moderate multiple texts for inappropriate content""" + try: + if "moderation" not in pipelines: + raise HTTPException(status_code=503, detail="Moderation pipeline not available") + + moderation = pipelines["moderation"] + if request.model_name: + from src.pipelines.moderation import ContentModerator + moderation = ContentModerator(request.model_name) + + results = [] + failed_count = 0 + + for text in request.texts: + try: + result = moderation.moderate(text) + if "error" in result: + failed_count += 1 + results.append(result) + except Exception as e: + failed_count += 1 + results.append({"text": text, "error": str(e)}) + + return BatchResponse( + success=True, + results=results, + processed_count=len(request.texts), + failed_count=failed_count + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/textgen/batch", response_model=BatchResponse) +async def generate_text_batch(request: TextListRequest): + """Generate text from multiple prompts""" + try: + if "textgen" not in pipelines: + raise HTTPException(status_code=503, detail="Text generation pipeline not available") + + textgen = pipelines["textgen"] + if request.model_name: + from src.pipelines.textgen import TextGenerator + textgen = TextGenerator(request.model_name) + + results = [] + failed_count = 0 + + for text in request.texts: + try: + result = textgen.generate(text) + if "error" in result: + failed_count += 1 + results.append(result) + except Exception as e: + failed_count += 1 + results.append({"text": text, "error": str(e)}) + + return BatchResponse( + success=True, + results=results, + processed_count=len(request.texts), + failed_count=failed_count + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) \ No newline at end of file diff --git a/src/api/config.py b/src/api/config.py new file mode 100644 index 0000000..f5372fc --- /dev/null +++ b/src/api/config.py @@ -0,0 +1,46 @@ +""" +API configuration settings +""" +from typing import Dict, Any +from src.config.settings import Config + + +class APIConfig: + """Configuration for the FastAPI application""" + + # Server settings + DEFAULT_HOST = "127.0.0.1" + DEFAULT_PORT = 8000 + + # API settings + API_TITLE = "AI Lab API" + API_DESCRIPTION = "API for various AI/ML pipelines using transformers" + API_VERSION = "1.0.0" + + # CORS settings + CORS_ORIGINS = ["*"] # Configure for production + CORS_METHODS = ["*"] + CORS_HEADERS = ["*"] + + # Pipeline settings + MAX_TEXT_LENGTH = 10000 + MAX_BATCH_SIZE = 100 + + @classmethod + def get_all_settings(cls) -> Dict[str, Any]: + """Get all configuration settings""" + return { + "server": { + "default_host": cls.DEFAULT_HOST, + "default_port": cls.DEFAULT_PORT + }, + "api": { + "title": cls.API_TITLE, + "description": cls.API_DESCRIPTION, + "version": cls.API_VERSION + }, + "limits": { + "max_text_length": cls.MAX_TEXT_LENGTH, + "max_batch_size": cls.MAX_BATCH_SIZE + }, + } \ No newline at end of file diff --git a/src/api/models.py b/src/api/models.py new file mode 100644 index 0000000..eae6336 --- /dev/null +++ b/src/api/models.py @@ -0,0 +1,91 @@ +""" +Pydantic models for API requests and responses +""" +from pydantic import BaseModel +from typing import List, Optional, Dict, Any + + +# Request models +class TextRequest(BaseModel): + """Base request model for single text input""" + text: str + model_name: Optional[str] = None + + +class TextListRequest(BaseModel): + """Request model for multiple texts""" + texts: List[str] + model_name: Optional[str] = None + + +class QARequest(BaseModel): + """Request model for question answering""" + question: str + context: str + model_name: Optional[str] = None + + +class FillMaskRequest(BaseModel): + """Request model for fill mask task""" + text: str + model_name: Optional[str] = None + + +# Response models +class BaseResponse(BaseModel): + """Base response model""" + success: bool + message: Optional[str] = None + + +class SentimentResponse(BaseResponse): + """Response model for sentiment analysis""" + text: str + sentiment: Optional[str] = None + confidence: Optional[float] = None + + +class NERResponse(BaseResponse): + """Response model for Named Entity Recognition""" + text: str + entities: Optional[List[Dict[str, Any]]] = None + + +class QAResponse(BaseResponse): + """Response model for Question Answering""" + question: str + context: str + answer: Optional[str] = None + confidence: Optional[float] = None + + +class FillMaskResponse(BaseResponse): + """Response model for Fill Mask""" + text: str + predictions: Optional[List[Dict[str, Any]]] = None + + +class ModerationResponse(BaseResponse): + """Response model for Content Moderation""" + text: str + flagged: Optional[bool] = None + categories: Optional[Dict[str, Any]] = None + + +class TextGenResponse(BaseResponse): + """Response model for Text Generation""" + prompt: str + generated_text: Optional[str] = None + + +class BatchResponse(BaseResponse): + """Response model for batch processing""" + results: List[Dict[str, Any]] + processed_count: int + failed_count: int + + +class ErrorResponse(BaseResponse): + """Response model for errors""" + error: str + details: Optional[str] = None \ No newline at end of file diff --git a/src/cli/display.py b/src/cli/display.py index df2dc27..6379541 100644 --- a/src/cli/display.py +++ b/src/cli/display.py @@ -190,3 +190,78 @@ class DisplayFormatter: output.append(f" • {entity} ({count}x)") return "\n".join(output) + + @staticmethod + def format_qa_result(result: Dict[str, Any]) -> str: + """Format Question Answering result for display""" + if "error" in result: + return f"❌ {result['error']}" + + output = [] + output.append(f"❓ Question: {result['question']}") + + # Confidence indicator + confidence = result['confidence'] + confidence_emoji = "✅" if result['is_confident'] else "⚠️" + confidence_bar = "█" * int(confidence * 10) + + output.append(f"{confidence_emoji} Answer: {result['answer']}") + output.append(f"📊 Confidence: {result['confidence_level']} ({confidence:.1%}) {confidence_bar}") + + if not result['is_confident']: + output.append("⚠️ Low confidence - answer might not be reliable") + + output.append(f"\n📍 Position: characters {result['start_position']}-{result['end_position']}") + output.append(f"📄 Context with answer highlighted:") + output.append(f" {result['highlighted_context']}") + + return "\n".join(output) + + @staticmethod + def format_qa_context_analysis(analysis: Dict[str, Any]) -> str: + """Format QA context analysis for display""" + if "error" in analysis: + return f"❌ {analysis['error']}" + + output = [] + output.append("✅ Context set successfully!") + output.append(f"📊 Context Statistics:") + + stats = analysis['context_stats'] + output.append(f" • Words: {stats['word_count']}") + output.append(f" • Sentences: ~{stats['sentence_count']}") + output.append(f" • Characters: {stats['character_count']}") + + if analysis['suggested_questions']: + output.append(f"\n💡 Suggested question types:") + for suggestion in analysis['suggested_questions']: + output.append(f" • {suggestion}") + + if analysis['tips']: + output.append(f"\n📝 Tips for good questions:") + for tip in analysis['tips']: + output.append(f" • {tip}") + + return "\n".join(output) + + @staticmethod + def format_qa_multiple_result(result: Dict[str, Any]) -> str: + """Format multiple QA results for display""" + if "error" in result: + return f"❌ {result['error']}" + + output = [] + output.append(f"📊 Multiple Questions Analysis") + output.append("=" * 50) + output.append(f"Total Questions: {result['total_questions']}") + output.append(f"Successfully Processed: {result['processed_questions']}") + output.append(f"Confident Answers: {result['confident_answers']}") + output.append(f"Average Confidence: {result['average_confidence']:.1%}") + + output.append(f"\n📋 Results:") + for qa_result in result['results']: + confidence_emoji = "✅" if qa_result['is_confident'] else "⚠️" + output.append(f"\n{qa_result['question_number']}. {qa_result['question']}") + output.append(f" {confidence_emoji} {qa_result['answer']} ({qa_result['confidence']:.1%})") + + return "\n".join(output) diff --git a/src/commands/__init__.py b/src/commands/__init__.py index e8ea5d3..344e3d6 100644 --- a/src/commands/__init__.py +++ b/src/commands/__init__.py @@ -6,5 +6,6 @@ from .fillmask import FillMaskCommand from .textgen import TextGenCommand from .moderation import ModerationCommand from .ner import NERCommand +from .qa import QACommand -__all__ = ['SentimentCommand', 'FillMaskCommand', 'TextGenCommand', 'ModerationCommand', 'NERCommand'] +__all__ = ['SentimentCommand', 'FillMaskCommand', 'TextGenCommand', 'ModerationCommand', 'NERCommand', 'QACommand'] diff --git a/src/commands/qa.py b/src/commands/qa.py new file mode 100644 index 0000000..28f4543 --- /dev/null +++ b/src/commands/qa.py @@ -0,0 +1,214 @@ +from src.cli.base import CLICommand +from src.cli.display import DisplayFormatter +from src.pipelines.qa import QuestionAnsweringSystem + + +class QACommand(CLICommand): + """Interactive Question Answering command""" + + def __init__(self): + self.qa_system = None + self.current_context = None + self.session_questions = [] + + @property + def name(self) -> str: + return "qa" + + @property + def description(self) -> str: + return "Question Answering - Ask questions about a given text" + + def _initialize_qa_system(self): + """Lazy initialization of the QA system""" + if self.qa_system is None: + print("🔄 Loading Question Answering model...") + self.qa_system = QuestionAnsweringSystem() + DisplayFormatter.show_success("QA model loaded!") + + def _show_instructions(self): + """Show usage instructions and examples""" + print("\n❓ Question Answering System") + print("Ask questions about a text context and get precise answers.") + print("\n📝 How it works:") + print(" 1. First, provide a context (text containing information)") + print(" 2. Then ask questions about that context") + print(" 3. The system extracts answers directly from the text") + print("\n💡 Example context:") + print(" 'Albert Einstein was born in 1879 in Germany. He developed the theory of relativity.'") + print("💡 Example questions:") + print(" - When was Einstein born?") + print(" - Where was Einstein born?") + print(" - What theory did Einstein develop?") + print("\n🎛️ Commands:") + print(" 'back' - Return to main menu") + print(" 'help' - Show these instructions") + print(" 'context' - Set new context") + print(" 'multi' - Ask multiple questions at once") + print(" 'session' - Review session history") + print(" 'settings' - Adjust confidence threshold") + print("-" * 70) + + def _set_context(self): + """Allow user to set or change the context""" + print("\n📄 Set Context") + print("Enter the text that will serve as context for your questions.") + print("You can enter multiple lines. Type 'done' when finished.") + print("-" * 50) + + lines = [] + while True: + line = input("📝 ").strip() + if line.lower() == 'done': + break + if line: + lines.append(line) + + if not lines: + DisplayFormatter.show_warning("No context provided") + return False + + self.current_context = " ".join(lines) + + # Analyze context + analysis = self.qa_system.interactive_qa(self.current_context) + if "error" in analysis: + DisplayFormatter.show_error(analysis["error"]) + return False + + formatted_analysis = DisplayFormatter.format_qa_context_analysis(analysis) + print(formatted_analysis) + + return True + + def _ask_single_question(self): + """Ask a single question about the current context""" + if not self.current_context: + DisplayFormatter.show_warning("Please set a context first using 'context' command") + return + + question = input("\n❓ Your question: ").strip() + + if not question: + DisplayFormatter.show_warning("Please enter a question") + return + + DisplayFormatter.show_loading("Finding answer...") + result = self.qa_system.answer(question, self.current_context) + + if "error" not in result: + self.session_questions.append(result) + + formatted_result = DisplayFormatter.format_qa_result(result) + print(formatted_result) + + def _multi_question_mode(self): + """Allow asking multiple questions at once""" + if not self.current_context: + DisplayFormatter.show_warning("Please set a context first using 'context' command") + return + + print("\n❓ Multiple Questions Mode") + print("Enter your questions one by one. Type 'done' when finished.") + print("-" * 50) + + questions = [] + while True: + question = input(f"Question #{len(questions)+1}: ").strip() + if question.lower() == 'done': + break + if question: + questions.append(question) + + if not questions: + DisplayFormatter.show_warning("No questions provided") + return + + DisplayFormatter.show_loading(f"Processing {len(questions)} questions...") + result = self.qa_system.answer_multiple(questions, self.current_context) + + if "error" not in result: + self.session_questions.extend(result["results"]) + + formatted_result = DisplayFormatter.format_qa_multiple_result(result) + print(formatted_result) + + def _show_session_history(self): + """Show the history of questions asked in this session""" + if not self.session_questions: + DisplayFormatter.show_warning("No questions asked in this session yet") + return + + print(f"\n📚 Session History ({len(self.session_questions)} questions)") + print("=" * 60) + + for i, qa in enumerate(self.session_questions, 1): + confidence_emoji = "✅" if qa["is_confident"] else "⚠️" + print(f"\n{i}. {qa['question']}") + print(f" {confidence_emoji} {qa['answer']} (confidence: {qa['confidence']:.1%})") + + def _adjust_settings(self): + """Allow user to adjust QA settings""" + current_threshold = self.qa_system.confidence_threshold + print(f"\n⚙️ Current Settings:") + print(f"Confidence threshold: {current_threshold:.2f}") + print("\nLower threshold = more answers accepted (less strict)") + print("Higher threshold = fewer answers accepted (more strict)") + + try: + new_threshold = input(f"Enter new threshold (0.0-1.0, current: {current_threshold}): ").strip() + if new_threshold: + threshold = float(new_threshold) + self.qa_system.set_confidence_threshold(threshold) + DisplayFormatter.show_success(f"Threshold set to {threshold:.2f}") + except ValueError: + DisplayFormatter.show_error("Invalid threshold value") + + def run(self): + """Run interactive Question Answering""" + self._initialize_qa_system() + self._show_instructions() + + while True: + if self.current_context: + context_preview = (self.current_context[:50] + "...") if len(self.current_context) > 50 else self.current_context + prompt = f"\n💬 [{context_preview}] Ask a question: " + else: + prompt = "\n💬 Enter command or set context first: " + + user_input = input(prompt).strip() + + if user_input.lower() == 'back': + break + elif user_input.lower() == 'help': + self._show_instructions() + continue + elif user_input.lower() == 'context': + self._set_context() + continue + elif user_input.lower() == 'multi': + self._multi_question_mode() + continue + elif user_input.lower() == 'session': + self._show_session_history() + continue + elif user_input.lower() == 'settings': + self._adjust_settings() + continue + + if not user_input: + DisplayFormatter.show_warning("Please enter a question or command") + continue + + # If we have a context and user input is not a command, treat it as a question + if self.current_context: + DisplayFormatter.show_loading("Finding answer...") + result = self.qa_system.answer(user_input, self.current_context) + + if "error" not in result: + self.session_questions.append(result) + + formatted_result = DisplayFormatter.format_qa_result(result) + print(formatted_result) + else: + DisplayFormatter.show_warning("Please set a context first using 'context' command") diff --git a/src/config/settings.py b/src/config/settings.py index 8d9b2e8..95ccbd1 100644 --- a/src/config/settings.py +++ b/src/config/settings.py @@ -3,6 +3,7 @@ Global project configuration """ from pathlib import Path from typing import Dict, Any +import torch class Config: @@ -14,11 +15,12 @@ class Config: # Default models DEFAULT_MODELS = { - "sentiment": "cardiffnlp/twitter-roberta-base-sentiment-latest", - "fillmask": "distilbert-base-uncased", - "textgen": "gpt2", - "moderation": "unitary/toxic-bert", - "ner": "dbmdz/bert-large-cased-finetuned-conll03-english", + "sentiment": "distilbert-base-uncased-finetuned-sst-2-english", + "fillmask": "bert-base-uncased", + "textgen": "gpt2", + "ner": "dslim/bert-base-NER", + "moderation":"unitary/toxic-bert", + "qa": "distilbert-base-cased-distilled-squad", } # Interface @@ -28,6 +30,7 @@ class Config: # Performance MAX_BATCH_SIZE = 32 DEFAULT_MAX_LENGTH = 512 + USE_GPU = torch.cuda.is_available() # Auto-detect GPU availability @classmethod def get_model(cls, pipeline_name: str) -> str: diff --git a/src/main.py b/src/main.py index aa16046..4067ca8 100644 --- a/src/main.py +++ b/src/main.py @@ -1,8 +1,9 @@ #!/usr/bin/env python3 """ -CLI entry point for AI Lab +Entry point for AI Lab - supports both CLI and API modes """ import sys +import argparse from pathlib import Path # Add parent directory to PYTHONPATH @@ -13,13 +14,14 @@ from src.commands import ( FillMaskCommand, ModerationCommand, NERCommand, + QACommand, SentimentCommand, TextGenCommand, ) -def main(): - """Main CLI function""" +def run_cli(): + """Run the CLI interface""" try: # Create CLI interface cli = InteractiveCLI() @@ -31,6 +33,7 @@ def main(): TextGenCommand, ModerationCommand, NERCommand, + QACommand, ] for command in commands_to_register: cli.register_command(command()) @@ -39,11 +42,100 @@ def main(): cli.run() except KeyboardInterrupt: - print("\n👋 Stopping program") + print("\n👋 Stopping CLI") except Exception as e: - print(f"❌ Error: {e}") + print(f"❌ CLI Error: {e}") sys.exit(1) +def run_api(host: str = "127.0.0.1", port: int = 8000, reload: bool = False): + """Run the FastAPI server""" + try: + import uvicorn + + print(f"🚀 Starting AI Lab API server...") + print(f"📡 Server will be available at: http://{host}:{port}") + print(f"📚 API documentation: http://{host}:{port}/docs") + print(f"🔄 Reload mode: {'enabled' if reload else 'disabled'}") + + # Load the main FastAPI application + try: + from src.api.app import app + app_module = "src.api.app:app" + print("📊 Loading AI Lab API with all pipelines") + except ImportError as e: + print(f"❌ Error: Could not load API application: {e}") + print("� Make sure FastAPI dependencies are installed:") + print(" poetry add fastapi uvicorn[standard] pydantic") + sys.exit(1) + + uvicorn.run( + app_module, + host=host, + port=port, + reload=reload, + log_level="info" + ) + except ImportError: + print("❌ FastAPI dependencies not installed. Please run: pip install fastapi uvicorn") + print("Or with poetry: poetry add fastapi uvicorn[standard]") + sys.exit(1) + except Exception as e: + print(f"❌ API Error: {e}") + sys.exit(1) + + +def main(): + """Main entry point with argument parsing""" + parser = argparse.ArgumentParser( + description="AI Lab - CLI and API for AI/ML pipelines", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + %(prog)s # Run CLI interface (default) + %(prog)s --mode cli # Run CLI interface explicitly + %(prog)s --mode api # Run API server + %(prog)s --mode api --port 8080 # Run API server on port 8080 + %(prog)s --mode api --reload # Run API server with auto-reload + """ + ) + + parser.add_argument( + "--mode", + choices=["cli", "api"], + default="cli", + help="Choose between CLI or API mode (default: cli)" + ) + + # API specific arguments + parser.add_argument( + "--host", + default="127.0.0.1", + help="API server host (default: 127.0.0.1)" + ) + + parser.add_argument( + "--port", + type=int, + default=8000, + help="API server port (default: 8000)" + ) + + parser.add_argument( + "--reload", + action="store_true", + help="Enable auto-reload for API development" + ) + + args = parser.parse_args() + + if args.mode == "cli": + print("🖥️ Starting CLI mode...") + run_cli() + elif args.mode == "api": + print("🌐 Starting API mode...") + run_api(host=args.host, port=args.port, reload=args.reload) + + if __name__ == "__main__": main() diff --git a/src/pipelines/__init__.py b/src/pipelines/__init__.py index 5ad9ab6..04a777b 100644 --- a/src/pipelines/__init__.py +++ b/src/pipelines/__init__.py @@ -6,6 +6,7 @@ from .fillmask import FillMaskAnalyzer from .textgen import TextGenerator from .moderation import ContentModerator from .ner import NamedEntityRecognizer +from .qa import QuestionAnsweringSystem from .template import TemplatePipeline -__all__ = ['SentimentAnalyzer', 'FillMaskAnalyzer', 'TextGenerator', 'ContentModerator', 'NamedEntityRecognizer', 'TemplatePipeline'] +__all__ = ['SentimentAnalyzer', 'FillMaskAnalyzer', 'TextGenerator', 'ContentModerator', 'NamedEntityRecognizer', 'QuestionAnsweringSystem', 'TemplatePipeline'] diff --git a/src/pipelines/fillmask.py b/src/pipelines/fillmask.py index 99817f8..eb39000 100644 --- a/src/pipelines/fillmask.py +++ b/src/pipelines/fillmask.py @@ -46,7 +46,7 @@ class FillMaskAnalyzer: mask_predictions = [ { "token": pred["token_str"], - "score": round(pred["score"], 4), + "score": round(float(pred["score"]), 4), "sequence": pred["sequence"] } for pred in mask_results @@ -66,7 +66,7 @@ class FillMaskAnalyzer: predictions = [ { "token": pred["token_str"], - "score": round(pred["score"], 4), + "score": round(float(pred["score"]), 4), "sequence": pred["sequence"] } for pred in results diff --git a/src/pipelines/moderation.py b/src/pipelines/moderation.py index 25738eb..b286ebc 100644 --- a/src/pipelines/moderation.py +++ b/src/pipelines/moderation.py @@ -70,7 +70,7 @@ class ContentModerator: "original_text": text, "moderated_text": text, "is_modified": False, - "toxic_score": toxic_score, + "toxic_score": float(toxic_score), "words_replaced": 0 } @@ -81,8 +81,8 @@ class ContentModerator: "original_text": text, "moderated_text": moderated_text, "is_modified": True, - "toxic_score": toxic_score, - "words_replaced": words_replaced + "toxic_score": float(toxic_score), + "words_replaced": int(words_replaced) } except Exception as e: diff --git a/src/pipelines/ner.py b/src/pipelines/ner.py index 2d331ef..a059474 100644 --- a/src/pipelines/ner.py +++ b/src/pipelines/ner.py @@ -58,9 +58,9 @@ class NamedEntityRecognizer: processed_entity = { "text": entity["word"], "label": entity_type, - "confidence": round(entity["score"], 4), - "start": entity["start"], - "end": entity["end"], + "confidence": round(float(entity["score"]), 4), + "start": int(entity["start"]), + "end": int(entity["end"]), "emoji": self.entity_colors.get(entity_type, "🏷️") } @@ -70,7 +70,7 @@ class NamedEntityRecognizer: if entity_type not in entity_stats: entity_stats[entity_type] = {"count": 0, "entities": []} entity_stats[entity_type]["count"] += 1 - entity_stats[entity_type]["entities"].append(entity["word"]) + entity_stats[entity_type]["entities"].append(str(entity["word"])) # Create highlighted text highlighted_text = self._highlight_entities(text, filtered_entities) @@ -81,7 +81,7 @@ class NamedEntityRecognizer: "entities": filtered_entities, "entity_stats": entity_stats, "total_entities": len(filtered_entities), - "confidence_threshold": confidence_threshold + "confidence_threshold": float(confidence_threshold) } except Exception as e: diff --git a/src/pipelines/qa.py b/src/pipelines/qa.py new file mode 100644 index 0000000..be3aecb --- /dev/null +++ b/src/pipelines/qa.py @@ -0,0 +1,266 @@ +from transformers import pipeline +from typing import Dict, List, Optional, Tuple +from src.config import Config +import re + + +class QuestionAnsweringSystem: + """Question Answering system using transformers""" + + def __init__(self, model_name: Optional[str] = None): + """ + Initialize the question-answering pipeline + + Args: + model_name: Name of the model to use (optional) + """ + self.model_name = model_name or Config.get_model("qa") + print(f"Loading Question Answering model: {self.model_name}") + self.pipeline = pipeline("question-answering", model=self.model_name) + print("QA model loaded successfully!") + + # Default confidence threshold + self.confidence_threshold = 0.1 + + def answer(self, question: str, context: str, max_answer_len: int = 50) -> Dict: + """ + Answer a question based on the given context + + Args: + question: Question to answer + context: Context text containing the answer + max_answer_len: Maximum length of the answer + + Returns: + Dictionary with answer, score, and position information + """ + if not question.strip(): + return {"error": "Empty question"} + + if not context.strip(): + return {"error": "Empty context"} + + try: + result = self.pipeline( + question=question, + context=context, + max_answer_len=max_answer_len + ) + + confidence_level = self._get_confidence_level(result["score"]) + highlighted_context = self._highlight_answer_in_context( + context, result["answer"], result["start"], result["end"] + ) + + return { + "question": question, + "context": context, + "answer": result["answer"], + "confidence": round(result["score"], 4), + "confidence_level": confidence_level, + "start_position": result["start"], + "end_position": result["end"], + "highlighted_context": highlighted_context, + "is_confident": result["score"] >= self.confidence_threshold + } + + except Exception as e: + return {"error": f"QA processing error: {str(e)}"} + + def _get_confidence_level(self, score: float) -> str: + """ + Convert numerical score to confidence level + + Args: + score: Confidence score (0-1) + + Returns: + Confidence level description + """ + if score >= 0.8: + return "Very High" + elif score >= 0.6: + return "High" + elif score >= 0.4: + return "Medium" + elif score >= 0.2: + return "Low" + else: + return "Very Low" + + def _highlight_answer_in_context(self, context: str, answer: str, start: int, end: int) -> str: + """ + Highlight the answer within the context + + Args: + context: Original context + answer: Extracted answer + start: Start position of answer + end: End position of answer + + Returns: + Context with highlighted answer + """ + if start < 0 or end > len(context): + return context + + before = context[:start] + highlighted_answer = f"**{answer}**" + after = context[end:] + + return before + highlighted_answer + after + + def answer_multiple(self, questions: List[str], context: str, max_answer_len: int = 50) -> Dict: + """ + Answer multiple questions for the same context + + Args: + questions: List of questions to answer + context: Context text + max_answer_len: Maximum length of answers + + Returns: + Dictionary with all answers and summary statistics + """ + if not questions: + return {"error": "No questions provided"} + + if not context.strip(): + return {"error": "Empty context"} + + results = [] + confident_answers = 0 + total_confidence = 0 + + for i, question in enumerate(questions, 1): + result = self.answer(question, context, max_answer_len) + + if "error" not in result: + results.append({ + "question_number": i, + **result + }) + + if result["is_confident"]: + confident_answers += 1 + total_confidence += result["confidence"] + + if not results: + return {"error": "No valid questions processed"} + + average_confidence = total_confidence / len(results) if results else 0 + + return { + "context": context, + "total_questions": len(questions), + "processed_questions": len(results), + "confident_answers": confident_answers, + "average_confidence": round(average_confidence, 4), + "confidence_threshold": self.confidence_threshold, + "results": results + } + + def interactive_qa(self, context: str) -> Dict: + """ + Prepare context for interactive Q&A session + + Args: + context: Context text for questions + + Returns: + Context analysis and preparation info + """ + if not context.strip(): + return {"error": "Empty context"} + + # Basic context analysis + word_count = len(context.split()) + sentence_count = len([s for s in context.split('.') if s.strip()]) + char_count = len(context) + + # Suggest question types based on content + suggested_questions = self._generate_question_suggestions(context) + + return { + "context": context, + "context_stats": { + "word_count": word_count, + "sentence_count": sentence_count, + "character_count": char_count + }, + "suggested_questions": suggested_questions, + "tips": [ + "Ask specific questions about facts mentioned in the text", + "Use question words: Who, What, When, Where, Why, How", + "Keep questions clear and focused", + "The answer should be present in the provided context" + ] + } + + def _generate_question_suggestions(self, context: str) -> List[str]: + """ + Generate suggested questions based on context analysis + + Args: + context: Context text + + Returns: + List of suggested question templates + """ + suggestions = [] + + # Check for common patterns and suggest relevant questions + if re.search(r'\b\d{4}\b', context): # Years + suggestions.append("When did [event] happen?") + + if re.search(r'\b[A-Z][a-z]+ [A-Z][a-z]+\b', context): # Names + suggestions.append("Who is [person name]?") + + if re.search(r'\b(founded|created|established|built)\b', context, re.IGNORECASE): + suggestions.append("Who founded/created [organization]?") + + if re.search(r'\b(located|situated|based)\b', context, re.IGNORECASE): + suggestions.append("Where is [place/organization] located?") + + if re.search(r'\b(because|due to|reason)\b', context, re.IGNORECASE): + suggestions.append("Why did [event] happen?") + + if re.search(r'\b(how|method|process)\b', context, re.IGNORECASE): + suggestions.append("How does [process] work?") + + if not suggestions: + suggestions = [ + "What is the main topic of this text?", + "Who are the key people mentioned?", + "What important events are described?" + ] + + return suggestions[:5] # Limit to 5 suggestions + + def set_confidence_threshold(self, threshold: float): + """ + Set the confidence threshold for answers + + Args: + threshold: Threshold between 0 and 1 + """ + if 0 <= threshold <= 1: + self.confidence_threshold = threshold + else: + raise ValueError("Threshold must be between 0 and 1") + + def answer_batch(self, qa_pairs: List[Tuple[str, str]], max_answer_len: int = 50) -> List[Dict]: + """ + Process multiple question-context pairs + + Args: + qa_pairs: List of (question, context) tuples + max_answer_len: Maximum length of answers + + Returns: + List of QA results + """ + return [ + self.answer(question, context, max_answer_len) + for question, context in qa_pairs + ] diff --git a/src/pipelines/textgen.py b/src/pipelines/textgen.py index 8c6b5bf..e29d7c8 100644 --- a/src/pipelines/textgen.py +++ b/src/pipelines/textgen.py @@ -15,17 +15,29 @@ class TextGenerator: """ self.model_name = model_name or Config.get_model("textgen") print(f"Loading text generation model: {self.model_name}") - self.pipeline = pipeline("text-generation", model=self.model_name) + + # Initialize pipeline with proper device configuration + self.pipeline = pipeline( + "text-generation", + model=self.model_name, + device=0 if Config.USE_GPU else -1, + torch_dtype="auto" + ) + + # Set pad token if not available + if self.pipeline.tokenizer.pad_token is None: + self.pipeline.tokenizer.pad_token = self.pipeline.tokenizer.eos_token + print("Model loaded successfully!") - def generate(self, prompt: str, max_length: int = 100, num_return_sequences: int = 1, + def generate(self, prompt: str, max_new_tokens: int = 100, num_return_sequences: int = 1, temperature: float = 1.0, do_sample: bool = True) -> Dict: """ Generate text from a prompt Args: prompt: Input text prompt - max_length: Maximum length of generated text + max_new_tokens: Maximum number of new tokens to generate num_return_sequences: Number of sequences to generate temperature: Sampling temperature (higher = more random) do_sample: Whether to use sampling @@ -39,11 +51,12 @@ class TextGenerator: try: results = self.pipeline( prompt, - max_length=max_length, + max_new_tokens=max_new_tokens, num_return_sequences=num_return_sequences, temperature=temperature, do_sample=do_sample, - pad_token_id=self.pipeline.tokenizer.eos_token_id + pad_token_id=self.pipeline.tokenizer.eos_token_id, + return_full_text=True ) generations = [ @@ -57,7 +70,7 @@ class TextGenerator: return { "prompt": prompt, "parameters": { - "max_length": max_length, + "max_new_tokens": max_new_tokens, "num_sequences": num_return_sequences, "temperature": temperature, "do_sample": do_sample diff --git a/ui/index.html b/ui/index.html new file mode 100644 index 0000000..1ca4ea6 --- /dev/null +++ b/ui/index.html @@ -0,0 +1,358 @@ + + + + + + + AI Lab - Interface de Test + + + + + + + +
+ +
+
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+ +
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+ API Déconnectée +
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⚙️ Configuration API

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💭 Analyse de Sentiment

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Analysez l'émotion et le ton d'un texte

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🏷️ Reconnaissance d'Entités Nommées

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Identifiez les personnes, lieux et organisations

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❓ Questions-Réponses

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Obtenez des réponses basées sur un contexte

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🎭 Complétion de Masques

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Prédisez les mots manquants avec [MASK]

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🛡️ Modération de Contenu

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Détectez le contenu toxique ou inapproprié

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✍️ Génération de Texte

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Générez du texte créatif à partir d'un prompt

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📦 Traitement par Lot

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Analysez plusieurs textes simultanément

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+ + + + + \ No newline at end of file diff --git a/ui/script.js b/ui/script.js new file mode 100644 index 0000000..233d0a9 --- /dev/null +++ b/ui/script.js @@ -0,0 +1,1057 @@ +// Configuration globale +let apiUrl = "http://localhost:8000"; + +// État de l'application +const appState = { + currentTab: "sentiment", + apiConnected: false +}; + +// Initialisation de l'application +document.addEventListener("DOMContentLoaded", function () { + initializeApp(); +}); + +function initializeApp() { + setupEventListeners(); + checkApiStatus(); + loadExamplesData(); +} + +function setupEventListeners() { + // Navigation entre les onglets + document.querySelectorAll(".nav-tab").forEach((tab) => { + tab.addEventListener("click", (e) => { + const tabName = e.currentTarget.dataset.tab; + switchTab(tabName); + }); + }); + + // Changement d'URL API + document.getElementById("apiUrl").addEventListener("change", function () { + apiUrl = this.value; + checkApiStatus(); + }); + + // Soumission des formulaires + setupFormHandlers(); +} + +function setupFormHandlers() { + // Empêcher la soumission par défaut de tous les formulaires + document.querySelectorAll("form").forEach((form) => { + form.addEventListener("submit", (e) => { + e.preventDefault(); + }); + }); +} + +// Navigation +function switchTab(tabName) { + // Mise à jour de l'état + appState.currentTab = tabName; + + // Mise à jour des onglets + document.querySelectorAll(".nav-tab").forEach((tab) => { + tab.classList.remove("active"); + }); + document.querySelector(`[data-tab="${tabName}"]`).classList.add("active"); + + // Mise à jour du contenu + document.querySelectorAll(".tab-content").forEach((content) => { + content.classList.remove("active"); + }); + document.getElementById(tabName).classList.add("active"); +} + +// Vérification du statut de l'API +async function checkApiStatus() { + const statusElement = document.getElementById("apiStatus"); + const indicator = statusElement.querySelector(".status-indicator"); + const statusText = statusElement.querySelector("span"); + const testButton = document.querySelector("button[onclick='checkApiStatus()']"); + + // Feedback visuel pendant le test + if (testButton) { + testButton.disabled = true; + testButton.innerHTML = '⏳Test en cours...'; + testButton.classList.add("loading"); + } + + // Indicateur de chargement + indicator.className = "status-indicator loading"; + statusText.textContent = "Test de connexion..."; + + try { + const controller = new AbortController(); + const timeoutId = setTimeout(() => controller.abort(), 5000); + + const response = await fetch(`${apiUrl}/health`, { + method: "GET", + signal: controller.signal + }); + + clearTimeout(timeoutId); + + if (response.ok) { + const healthData = await response.json(); + appState.apiConnected = true; + indicator.className = "status-indicator online"; + statusText.textContent = "API Connectée"; + + // Notification de succès + showNotification("✅ Connexion API établie avec succès!", "success"); + + // Afficher les détails de l'API + showApiDetails(healthData); + } else { + throw new Error(`Erreur HTTP ${response.status}`); + } + } catch (error) { + appState.apiConnected = false; + indicator.className = "status-indicator offline"; + statusText.textContent = "API Déconnectée"; + + let errorMessage = "❌ Impossible de se connecter à l'API"; + if (error.name === "AbortError") { + errorMessage += " (Timeout)"; + } else if (error.message.includes("fetch")) { + errorMessage += " (Serveur inaccessible)"; + } else { + errorMessage += ` (${error.message})`; + } + + showNotification(errorMessage, "error"); + console.warn("Erreur de connexion API:", error.message); + } finally { + // Restaurer le bouton + if (testButton) { + testButton.disabled = false; + testButton.innerHTML = '🔄Tester la connexion'; + testButton.classList.remove("loading"); + } + } +} + +// Système de notifications +function showNotification(message, type = "info", duration = 5000) { + // Créer le conteneur de notifications s'il n'existe pas + let notificationContainer = document.getElementById("notification-container"); + if (!notificationContainer) { + notificationContainer = document.createElement("div"); + notificationContainer.id = "notification-container"; + notificationContainer.className = "notification-container"; + document.body.appendChild(notificationContainer); + } + + // Créer la notification + const notification = document.createElement("div"); + notification.className = `notification notification-${type}`; + notification.innerHTML = ` +
+ ${message} + +
+ `; + + // Ajouter l'animation d'entrée + notification.style.transform = "translateX(100%)"; + notification.style.opacity = "0"; + notificationContainer.appendChild(notification); + + // Animation d'entrée + setTimeout(() => { + notification.style.transform = "translateX(0)"; + notification.style.opacity = "1"; + }, 10); + + // Suppression automatique + if (duration > 0) { + setTimeout(() => { + if (notification.parentElement) { + notification.style.transform = "translateX(100%)"; + notification.style.opacity = "0"; + setTimeout(() => notification.remove(), 300); + } + }, duration); + } +} + +// Affichage des détails de l'API +function showApiDetails(healthData) { + const existingDetails = document.getElementById("api-details"); + if (existingDetails) { + existingDetails.remove(); + } + + const configSection = document.querySelector(".config-section .config-card"); + const detailsDiv = document.createElement("div"); + detailsDiv.id = "api-details"; + detailsDiv.className = "api-details"; + detailsDiv.innerHTML = ` +
+

📊 Détails de l'API

+ +
+
+
+ Statut: + ✅ ${healthData.status || "healthy"} +
+
+ Pipelines chargés: + ${healthData.pipelines_loaded || 0} +
+
+ Pipelines disponibles: + ${(healthData.available_pipelines || []).join(", ") || "Aucun"} +
+
+ `; + + configSection.appendChild(detailsDiv); +} +async function makeApiRequest(endpoint, data) { + if (!appState.apiConnected) { + throw new Error("API non connectée. Vérifiez la configuration."); + } + + try { + const response = await fetch(`${apiUrl}${endpoint}`, { + method: "POST", + headers: { + "Content-Type": "application/json" + }, + body: JSON.stringify(data) + }); + + const result = await response.json(); + + if (!response.ok) { + throw new Error(result.detail || `Erreur ${response.status}`); + } + + return result; + } catch (error) { + if (error.name === "TypeError" && error.message.includes("fetch")) { + throw new Error("Impossible de contacter l'API. Vérifiez que le serveur est démarré."); + } + throw error; + } +} + +// Gestion de l'affichage des résultats améliorée +function showLoading(containerId) { + const container = document.getElementById(containerId); + container.innerHTML = ` +
+ +
+

🔄 Traitement en cours...

+

Analyse de votre texte par l'IA

+
+
+
+
+
+ `; + container.classList.add("show"); +} + +function showResult(containerId, data, isError = false) { + const container = document.getElementById(containerId); + const headerClass = isError ? "error" : "success"; + const icon = isError ? "❌" : "✅"; + const title = isError ? "Erreur" : "Résultat"; + + let formattedContent; + if (isError) { + formattedContent = formatErrorResult(data); + } else { + formattedContent = formatResult(data, containerId); + } + + // Ajouter un timestamp + const timestamp = new Date().toLocaleTimeString("fr-FR"); + + container.innerHTML = ` +
+
+
+ ${icon} +
+ ${title} + ${timestamp} +
+
+
+ + + +
+
+
+ ${formattedContent} +
+
+ `; + container.classList.add("show"); + + // Animation d'entrée + const resultCard = container.querySelector(".result-card"); + resultCard.style.transform = "translateY(20px)"; + resultCard.style.opacity = "0"; + setTimeout(() => { + resultCard.style.transform = "translateY(0)"; + resultCard.style.opacity = "1"; + }, 10); +} + +function formatErrorResult(error) { + let errorMessage = "Une erreur inattendue s'est produite"; + let suggestions = []; + + if (typeof error === "object" && error.error) { + errorMessage = error.error; + + // Suggestions basées sur le type d'erreur + if (errorMessage.includes("API non connectée")) { + suggestions.push("Vérifiez que le serveur API est démarré"); + suggestions.push("Testez la connexion avec le bouton 'Tester la connexion'"); + } else if (errorMessage.includes("requis")) { + suggestions.push("Assurez-vous que tous les champs obligatoires sont remplis"); + } else if (errorMessage.includes("MASK")) { + suggestions.push("Utilisez [MASK] dans votre texte pour le fill-mask"); + } + } + + let suggestionsHtml = ""; + if (suggestions.length > 0) { + suggestionsHtml = ` +
+

💡 Suggestions:

+ +
+ `; + } + + return ` +
+
+ Message d'erreur: +

${errorMessage}

+
+ ${suggestionsHtml} +
+ `; +} + +function formatResult(data, containerId) { + const type = containerId.replace("Result", ""); + + switch (type) { + case "sentiment": + return formatSentimentResult(data); + case "ner": + return formatNerResult(data); + case "qa": + return formatQaResult(data); + case "fillmask": + return formatFillmaskResult(data); + case "moderation": + return formatModerationResult(data); + case "textgen": + return formatTextgenResult(data); + case "batch": + return formatBatchResult(data); + default: + return `
${JSON.stringify(data, null, 2)}
`; + } +} + +function formatSentimentResult(data) { + const sentiment = data.sentiment || data.label; + const confidence = data.confidence || data.score; + const badgeClass = sentiment?.toLowerCase() === "positive" ? "positive" : sentiment?.toLowerCase() === "negative" ? "negative" : "neutral"; + + // Calcul de la barre de progression pour la confiance + const confidencePercent = confidence ? (confidence * 100).toFixed(1) : 0; + const progressColor = confidencePercent > 80 ? "var(--success-500)" : confidencePercent > 60 ? "var(--warning-500)" : "var(--error-500)"; + + return ` +
+
+
+ Sentiment détecté: + ${sentiment || "Non déterminé"} +
+
+
+ Niveau de confiance: + ${confidencePercent}% +
+
+
+
+
+
+
+ ${getSentimentInterpretation(sentiment, confidence)} +
+
+ 🔍 Détails techniques +
${JSON.stringify(data, null, 2)}
+
+
+ `; +} + +function formatNerResult(data) { + let entitiesHtml = ""; + let entitiesStats = {}; + + if (data.entities && data.entities.length > 0) { + // Compter les entités par type + data.entities.forEach((entity) => { + const label = entity.label; + entitiesStats[label] = (entitiesStats[label] || 0) + 1; + }); + + entitiesHtml = data.entities + .map((entity, index) => { + const label = entity.label?.toLowerCase() || "misc"; + const confidence = entity.confidence ? (entity.confidence * 100).toFixed(1) : null; + return ` +
+ + ${entity.text} + + ${entity.label} + ${confidence ? `${confidence}%` : ""} +
+ `; + }) + .join(""); + } else { + entitiesHtml = `
🔍 Aucune entité nommée détectée dans ce texte
`; + } + + // Statistiques des entités + const statsHtml = + Object.keys(entitiesStats).length > 0 + ? ` +
+

📊 Statistiques des entités:

+
+ ${Object.entries(entitiesStats) + .map( + ([type, count]) => ` +
+ ${type} + ${count} +
+ ` + ) + .join("")} +
+
+ ` + : ""; + + return ` +
+
+

🏷️ Entités détectées:

+
+ ${entitiesHtml} +
+
+ ${statsHtml} +
+ 🔍 Détails techniques +
${JSON.stringify(data, null, 2)}
+
+
+ `; +} + +function formatQaResult(data) { + const confidence = data.confidence ? (data.confidence * 100).toFixed(1) : null; + const progressColor = confidence > 80 ? "var(--success-500)" : confidence > 60 ? "var(--warning-500)" : "var(--error-500)"; + + return ` +
+
+
+

❓ Question:

+

${data.question}

+
+
+

💡 Réponse:

+
${data.answer || "Aucune réponse trouvée dans le contexte fourni"}
+
+ ${ + confidence + ? ` +
+
+ Fiabilité de la réponse: + ${confidence}% +
+
+
+
+
+ ` + : "" + } +
+
+ 🔍 Détails techniques +
${JSON.stringify(data, null, 2)}
+
+
+ `; +} + +function formatFillmaskResult(data) { + let predictionsHtml = ""; + if (data.predictions && data.predictions.length > 0) { + predictionsHtml = data.predictions + .map((pred, index) => { + const score = pred.score ? (pred.score * 100).toFixed(1) : null; + const rankClass = index === 0 ? "rank-first" : index === 1 ? "rank-second" : "rank-other"; + return ` +
+
#${index + 1}
+
+ ${pred.token || pred.token_str} + ${score ? `${score}%` : ""} +
+ ${ + score + ? ` +
+
+
+ ` + : "" + } +
+ `; + }) + .join(""); + } else { + predictionsHtml = `
🔍 Aucune prédiction disponible
`; + } + + return ` +
+
+

🎭 Mots prédits pour remplacer [MASK]:

+
+ ${predictionsHtml} +
+
+
+ 🔍 Détails techniques +
${JSON.stringify(data, null, 2)}
+
+
+ `; +} + +function formatModerationResult(data) { + const flagged = data.flagged; + const badgeClass = flagged ? "negative" : "positive"; + const status = flagged ? "CONTENU SIGNALÉ" : "CONTENU APPROPRIÉ"; + const icon = flagged ? "⚠️" : "✅"; + + let categoriesHtml = ""; + if (data.categories) { + const categories = Object.entries(data.categories); + if (categories.length > 0) { + categoriesHtml = ` +
+

📋 Détails de l'analyse:

+
+ ${categories + .map(([key, value]) => { + let displayKey = key; + let displayValue = value; + + if (key === "toxic_score") { + displayKey = "Score de toxicité"; + displayValue = typeof value === "number" ? `${(value * 100).toFixed(1)}%` : value; + } else if (key === "is_modified") { + displayKey = "Contenu modifié"; + displayValue = value ? "Oui" : "Non"; + } else if (key === "words_replaced") { + displayKey = "Mots remplacés"; + } else if (key === "restored_text") { + displayKey = "Texte restauré"; + } + + return ` +
+ ${displayKey}: + ${displayValue} +
+ `; + }) + .join("")} +
+
+ `; + } + } + + return ` +
+
+ ${icon} + ${status} +
+ ${categoriesHtml} +
+ 🔍 Détails techniques +
${JSON.stringify(data, null, 2)}
+
+
+ `; +} + +function formatTextgenResult(data) { + const generatedText = data.generated_text || "Aucun texte généré"; + const prompt = data.prompt || ""; + + return ` +
+
+

📝 Prompt initial:

+
${prompt}
+
+
+

✨ Texte généré:

+
+
${generatedText}
+
+ + +
+
+
+
+ 🔍 Détails techniques +
${JSON.stringify(data, null, 2)}
+
+
+ `; +} + +function formatBatchResult(data) { + if (!data.results || data.results.length === 0) { + return "Aucun résultat"; + } + + const resultsHtml = data.results + .map((result, index) => { + return ` +
+
Résultat ${index + 1}
+
${JSON.stringify(result, null, 2)}
+
+ `; + }) + .join(""); + + return ` +
+ Résumé: ${data.processed_count} traités, ${data.failed_count} échecs +
+
+ ${resultsHtml} +
+ `; +} + +// Fonctions de traitement pour chaque endpoint +async function analyzeSentiment(event) { + event.preventDefault(); + showLoading("sentimentResult"); + + const text = document.getElementById("sentimentText").value.trim(); + const model = document.getElementById("sentimentModel").value; + + if (!text) { + showResult("sentimentResult", { error: "Le texte est requis" }, true); + return; + } + + try { + const data = { text }; + if (model) data.model_name = model; + + const result = await makeApiRequest("/sentiment", data); + showResult("sentimentResult", result); + } catch (error) { + showResult("sentimentResult", { error: error.message }, true); + } +} + +async function analyzeNER(event) { + event.preventDefault(); + showLoading("nerResult"); + + const text = document.getElementById("nerText").value.trim(); + const model = document.getElementById("nerModel").value; + + if (!text) { + showResult("nerResult", { error: "Le texte est requis" }, true); + return; + } + + try { + const data = { text }; + if (model) data.model_name = model; + + const result = await makeApiRequest("/ner", data); + showResult("nerResult", result); + } catch (error) { + showResult("nerResult", { error: error.message }, true); + } +} + +async function answerQuestion(event) { + event.preventDefault(); + showLoading("qaResult"); + + const question = document.getElementById("qaQuestion").value.trim(); + const context = document.getElementById("qaContext").value.trim(); + const model = document.getElementById("qaModel").value; + + if (!question || !context) { + showResult("qaResult", { error: "La question et le contexte sont requis" }, true); + return; + } + + try { + const data = { question, context }; + if (model) data.model_name = model; + + const result = await makeApiRequest("/qa", data); + showResult("qaResult", result); + } catch (error) { + showResult("qaResult", { error: error.message }, true); + } +} + +async function fillMask(event) { + event.preventDefault(); + showLoading("fillmaskResult"); + + const text = document.getElementById("fillmaskText").value.trim(); + const model = document.getElementById("fillmaskModel").value; + + if (!text) { + showResult("fillmaskResult", { error: "Le texte est requis" }, true); + return; + } + + if (!text.includes("[MASK]")) { + showResult("fillmaskResult", { error: "Le texte doit contenir [MASK]" }, true); + return; + } + + try { + const data = { text }; + if (model) data.model_name = model; + + const result = await makeApiRequest("/fillmask", data); + showResult("fillmaskResult", result); + } catch (error) { + showResult("fillmaskResult", { error: error.message }, true); + } +} + +async function moderateContent(event) { + event.preventDefault(); + showLoading("moderationResult"); + + const text = document.getElementById("moderationText").value.trim(); + const model = document.getElementById("moderationModel").value; + + if (!text) { + showResult("moderationResult", { error: "Le texte est requis" }, true); + return; + } + + try { + const data = { text }; + if (model) data.model_name = model; + + const result = await makeApiRequest("/moderation", data); + showResult("moderationResult", result); + } catch (error) { + showResult("moderationResult", { error: error.message }, true); + } +} + +async function generateText(event) { + event.preventDefault(); + showLoading("textgenResult"); + + const text = document.getElementById("textgenPrompt").value.trim(); + const model = document.getElementById("textgenModel").value; + + if (!text) { + showResult("textgenResult", { error: "Le prompt est requis" }, true); + return; + } + + try { + const data = { text }; + if (model) data.model_name = model; + + const result = await makeApiRequest("/textgen", data); + showResult("textgenResult", result); + } catch (error) { + showResult("textgenResult", { error: error.message }, true); + } +} + +async function processBatch() { + showLoading("batchResult"); + + const type = document.getElementById("batchType").value; + const textsInput = document.getElementById("batchTexts").value.trim(); + + if (!textsInput) { + showResult("batchResult", { error: "Les textes sont requis" }, true); + return; + } + + const texts = textsInput + .split("\n") + .filter((line) => line.trim()) + .map((line) => line.trim()); + + if (texts.length === 0) { + showResult("batchResult", { error: "Aucun texte valide fourni" }, true); + return; + } + + try { + const data = { texts }; + const result = await makeApiRequest(`/${type}/batch`, data); + showResult("batchResult", result); + } catch (error) { + showResult("batchResult", { error: error.message }, true); + } +} + +// Exemples prédéfinis (en anglais pour optimiser la compatibilité avec les modèles) +const examples = { + sentiment: "I love this project! It's really well designed and useful for testing NLP APIs.", + ner: "Apple Inc. is an American multinational technology company headquartered in Cupertino, California. Tim Cook is the current CEO of Apple.", + qa: { + question: "What is the capital of France?", + context: + "France is a country located in Western Europe. Paris is the capital and largest city of France. The city is famous for the Eiffel Tower and the Louvre Museum." + }, + fillmask: "The capital of France is [MASK].", + moderation: "This project is fantastic! Thank you for this excellent work.", + textgen: "Once upon a time, in a distant kingdom,", + batch: `I love this product! +This is really terrible. +Not bad at all. +Excellent work! +I hate all of this.` +}; + +function loadExample(type) { + const example = examples[type]; + + if (!example) return; + + switch (type) { + case "sentiment": + document.getElementById("sentimentText").value = example; + break; + case "ner": + document.getElementById("nerText").value = example; + break; + case "qa": + document.getElementById("qaQuestion").value = example.question; + document.getElementById("qaContext").value = example.context; + break; + case "fillmask": + document.getElementById("fillmaskText").value = example; + break; + case "moderation": + document.getElementById("moderationText").value = example; + break; + case "textgen": + document.getElementById("textgenPrompt").value = example; + break; + case "batch": + document.getElementById("batchTexts").value = example; + break; + } +} + +function loadExamplesData() { + // Cette fonction peut être étendue pour charger des exemples depuis une source externe + console.log("Exemples chargés"); +} + +// Fonctions utilitaires pour l'UX +function getSentimentInterpretation(sentiment, confidence) { + const confidenceLevel = confidence > 0.8 ? "très élevée" : confidence > 0.6 ? "élevée" : confidence > 0.4 ? "modérée" : "faible"; + + let interpretation = ""; + if (sentiment?.toLowerCase() === "positive") { + interpretation = `😊 Le texte exprime un sentiment positif avec une confiance ${confidenceLevel}.`; + } else if (sentiment?.toLowerCase() === "negative") { + interpretation = `😔 Le texte exprime un sentiment négatif avec une confiance ${confidenceLevel}.`; + } else { + interpretation = `😐 Le sentiment du texte est neutre avec une confiance ${confidenceLevel}.`; + } + + return `
${interpretation}
`; +} + +function copyToClipboard(text) { + navigator.clipboard + .writeText(text) + .then(() => { + showNotification("📋 Texte copié dans le presse-papiers!", "success", 2000); + }) + .catch(() => { + showNotification("❌ Impossible de copier le texte", "error", 2000); + }); +} + +function copyResultToClipboard(containerId) { + const container = document.getElementById(containerId); + const resultData = container.querySelector(".result-json"); + if (resultData) { + copyToClipboard(resultData.textContent); + } +} + +function toggleResultDetails(containerId) { + const container = document.getElementById(containerId); + const details = container.querySelector(".result-details"); + if (details) { + details.open = !details.open; + } +} + +function regenerateText() { + const currentPrompt = document.getElementById("textgenPrompt").value; + if (currentPrompt) { + const form = document.querySelector("#textgen form"); + const event = new Event("submit"); + generateText(event); + } +} + +// Amélioration des fonctions de traitement avec feedback +async function analyzeSentiment(event) { + event.preventDefault(); + showLoading("sentimentResult"); + + const text = document.getElementById("sentimentText").value.trim(); + const model = document.getElementById("sentimentModel").value; + + if (!text) { + showResult("sentimentResult", { error: "Le texte est requis pour l'analyse de sentiment" }, true); + return; + } + + try { + showNotification("🔄 Analyse de sentiment en cours...", "info", 2000); + + const data = { text }; + if (model) data.model_name = model; + + const result = await makeApiRequest("/sentiment", data); + showResult("sentimentResult", result); + showNotification("✅ Analyse de sentiment terminée!", "success", 3000); + } catch (error) { + showResult("sentimentResult", { error: error.message }, true); + showNotification("❌ Erreur lors de l'analyse de sentiment", "error", 4000); + } +} + +async function analyzeNER(event) { + event.preventDefault(); + showLoading("nerResult"); + + const text = document.getElementById("nerText").value.trim(); + const model = document.getElementById("nerModel").value; + + if (!text) { + showResult("nerResult", { error: "Le texte est requis pour la reconnaissance d'entités" }, true); + return; + } + + try { + showNotification("🔄 Reconnaissance d'entités en cours...", "info", 2000); + + const data = { text }; + if (model) data.model_name = model; + + const result = await makeApiRequest("/ner", data); + showResult("nerResult", result); + + const entityCount = result.entities ? result.entities.length : 0; + showNotification(`✅ ${entityCount} entité(s) détectée(s)!`, "success", 3000); + } catch (error) { + showResult("nerResult", { error: error.message }, true); + showNotification("❌ Erreur lors de la reconnaissance d'entités", "error", 4000); + } +} + +async function answerQuestion(event) { + event.preventDefault(); + showLoading("qaResult"); + + const question = document.getElementById("qaQuestion").value.trim(); + const context = document.getElementById("qaContext").value.trim(); + const model = document.getElementById("qaModel").value; + + if (!question || !context) { + showResult("qaResult", { error: "La question et le contexte sont requis" }, true); + return; + } + + try { + showNotification("🔄 Recherche de réponse en cours...", "info", 2000); + + const data = { question, context }; + if (model) data.model_name = model; + + const result = await makeApiRequest("/qa", data); + showResult("qaResult", result); + showNotification("✅ Réponse trouvée!", "success", 3000); + } catch (error) { + showResult("qaResult", { error: error.message }, true); + showNotification("❌ Erreur lors de la recherche de réponse", "error", 4000); + } +} diff --git a/ui/style.css b/ui/style.css new file mode 100644 index 0000000..e81901d --- /dev/null +++ b/ui/style.css @@ -0,0 +1,1436 @@ +/* Reset et variables CSS */ +:root { + /* Couleurs principales */ + --primary-50: #f0f9ff; + --primary-100: #e0f2fe; + --primary-500: #0ea5e9; + --primary-600: #0284c7; + --primary-700: #0369a1; + + /* Couleurs neutres */ + --gray-50: #f9fafb; + --gray-100: #f3f4f6; + --gray-200: #e5e7eb; + --gray-300: #d1d5db; + --gray-400: #9ca3af; + --gray-500: #6b7280; + --gray-600: #4b5563; + --gray-700: #374151; + --gray-800: #1f2937; + --gray-900: #111827; + + /* Couleurs de statut */ + --success-50: #f0fdf4; + --success-100: #dcfce7; + --success-500: #22c55e; + --success-600: #16a34a; + + --warning-50: #fffbeb; + --warning-100: #fef3c7; + --warning-500: #f59e0b; + --warning-600: #d97706; + + --error-50: #fef2f2; + --error-100: #fee2e2; + --error-500: #ef4444; + --error-600: #dc2626; + + /* Espacements */ + --space-1: 0.25rem; + --space-2: 0.5rem; + --space-3: 0.75rem; + --space-4: 1rem; + --space-6: 1.5rem; + --space-8: 2rem; + --space-12: 3rem; + + /* Border radius */ + --radius-sm: 0.375rem; + --radius: 0.5rem; + --radius-lg: 0.75rem; + --radius-xl: 1rem; + + /* Ombres */ + --shadow-sm: 0 1px 2px 0 rgb(0 0 0 / 0.05); + --shadow: 0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1); + --shadow-lg: 0 10px 15px -3px rgb(0 0 0 / 0.1), 0 4px 6px -4px rgb(0 0 0 / 0.1); + --shadow-xl: 0 20px 25px -5px rgb(0 0 0 / 0.1), 0 8px 10px -6px rgb(0 0 0 / 0.1); + + /* Transitions */ + --transition: all 0.2s cubic-bezier(0.4, 0, 0.2, 1); +} + +/* Reset */ +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +html { + font-size: 16px; + -webkit-text-size-adjust: 100%; +} + +body { + font-family: "Inter", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; + line-height: 1.6; + color: var(--gray-700); + background-color: var(--gray-50); + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +/* Layout */ +.app { + min-height: 100vh; + display: flex; + flex-direction: column; +} + +.container { + max-width: 1200px; + margin: 0 auto; + padding: 0 var(--space-4); + width: 100%; +} + +/* Header */ +.header { + background: white; + border-bottom: 1px solid var(--gray-200); + box-shadow: var(--shadow-sm); + position: sticky; + top: 0; + z-index: 100; +} + +.header-content { + display: flex; + justify-content: space-between; + align-items: center; + padding: var(--space-4) 0; +} + +.logo h1 { + font-size: 1.5rem; + font-weight: 700; + color: var(--primary-600); + margin-bottom: var(--space-1); +} + +.logo p { + color: var(--gray-500); + font-size: 0.875rem; +} + +.api-status { + display: flex; + align-items: center; + gap: var(--space-2); + padding: var(--space-2) var(--space-3); + background: var(--gray-50); + border-radius: var(--radius); + font-size: 0.875rem; + font-weight: 500; +} + +.status-indicator { + width: 8px; + height: 8px; + border-radius: 50%; + transition: var(--transition); +} + +.status-indicator.online { + background: var(--success-500); + box-shadow: 0 0 0 2px var(--success-100); +} + +.status-indicator.offline { + background: var(--error-500); + box-shadow: 0 0 0 2px var(--error-100); +} + +.status-indicator.loading { + background: var(--warning-500); + box-shadow: 0 0 0 2px var(--warning-100); + animation: pulse 2s infinite; +} + +@keyframes pulse { + 0%, + 100% { + opacity: 1; + } + 50% { + opacity: 0.5; + } +} + +/* Système de notifications */ +.notification-container { + position: fixed; + top: var(--space-4); + right: var(--space-4); + z-index: 1000; + max-width: 400px; +} + +.notification { + background: white; + border-radius: var(--radius-lg); + box-shadow: var(--shadow-xl); + margin-bottom: var(--space-2); + overflow: hidden; + transition: all 0.3s ease; + border-left: 4px solid; +} + +.notification-info { + border-left-color: var(--primary-500); +} + +.notification-success { + border-left-color: var(--success-500); +} + +.notification-warning { + border-left-color: var(--warning-500); +} + +.notification-error { + border-left-color: var(--error-500); +} + +.notification-content { + display: flex; + align-items: flex-start; + padding: var(--space-4); + gap: var(--space-3); +} + +.notification-message { + flex: 1; + font-size: 0.875rem; + line-height: 1.5; + color: var(--gray-700); +} + +.notification-close { + background: none; + border: none; + color: var(--gray-400); + cursor: pointer; + font-size: 1.25rem; + line-height: 1; + padding: 0; + width: 20px; + height: 20px; + display: flex; + align-items: center; + justify-content: center; + border-radius: var(--radius-sm); + transition: var(--transition); +} + +.notification-close:hover { + color: var(--gray-600); + background: var(--gray-100); +} + +/* Détails de l'API */ +.api-details { + margin-top: var(--space-4); + background: var(--success-50); + border: 1px solid var(--success-200); + border-radius: var(--radius); + padding: var(--space-4); + animation: slideDown 0.3s ease; +} + +@keyframes slideDown { + from { + opacity: 0; + transform: translateY(-10px); + } + to { + opacity: 1; + transform: translateY(0); + } +} + +.api-details-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: var(--space-3); +} + +.api-details-header h4 { + color: var(--success-700); + font-size: 0.875rem; + font-weight: 600; + margin: 0; +} + +.btn-minimal { + background: none; + border: none; + color: var(--gray-500); + cursor: pointer; + font-size: 1rem; + padding: var(--space-1); + border-radius: var(--radius-sm); + transition: var(--transition); +} + +.btn-minimal:hover { + color: var(--gray-700); + background: var(--gray-100); +} + +.api-details-content { + display: flex; + flex-direction: column; + gap: var(--space-2); +} + +.detail-item { + display: flex; + justify-content: space-between; + align-items: center; + font-size: 0.875rem; +} + +.detail-label { + color: var(--gray-600); + font-weight: 500; +} + +.detail-value { + color: var(--gray-700); + font-weight: 400; +} + +.detail-value.success { + color: var(--success-600); +} + +/* Améliorations de chargement */ +.loading-container { + display: flex; + align-items: center; + gap: var(--space-4); + padding: var(--space-6); + text-align: center; + background: linear-gradient(135deg, var(--primary-50) 0%, var(--gray-50) 100%); + border-radius: var(--radius-lg); + border: 1px solid var(--primary-100); +} + +.loading-spinner { + display: flex; + align-items: center; + justify-content: center; +} + +.loading-content { + flex: 1; + text-align: left; +} + +.loading-content h4 { + color: var(--primary-700); + margin-bottom: var(--space-2); + font-size: 1.125rem; +} + +.loading-content p { + color: var(--gray-600); + font-size: 0.875rem; + margin-bottom: var(--space-3); +} + +.loading-progress { + background: var(--gray-200); + height: 4px; + border-radius: 2px; + overflow: hidden; +} + +.progress-bar { + height: 100%; + background: linear-gradient(90deg, var(--primary-500), var(--primary-600)); + border-radius: 2px; + animation: progressSlide 2s ease-in-out infinite; +} + +@keyframes progressSlide { + 0% { + transform: translateX(-100%); + } + 100% { + transform: translateX(400%); + } +} + +/* Main */ +.main { + flex: 1; + padding: var(--space-8) 0; +} + +/* Configuration */ +.config-section { + margin-bottom: var(--space-8); +} + +.config-card { + background: white; + border: 1px solid var(--gray-200); + border-radius: var(--radius-xl); + padding: var(--space-6); + box-shadow: var(--shadow-sm); +} + +.config-card h3 { + font-size: 1.125rem; + font-weight: 600; + color: var(--gray-900); + margin-bottom: var(--space-4); +} + +.config-form { + display: flex; + gap: var(--space-4); + align-items: end; +} + +.config-form .input-group { + flex: 1; +} + +/* Navigation */ +.nav-section { + margin-bottom: var(--space-8); +} + +.nav-tabs { + display: flex; + gap: var(--space-2); + background: white; + border: 1px solid var(--gray-200); + border-radius: var(--radius-xl); + padding: var(--space-1); + box-shadow: var(--shadow-sm); + overflow-x: auto; +} + +.nav-tab { + flex: 1; + min-width: 120px; + display: flex; + align-items: center; + justify-content: center; + gap: var(--space-2); + padding: var(--space-3) var(--space-4); + border: none; + background: transparent; + border-radius: var(--radius-lg); + cursor: pointer; + font-weight: 500; + font-size: 0.875rem; + color: var(--gray-600); + transition: var(--transition); + white-space: nowrap; +} + +.nav-tab:hover { + background: var(--gray-50); + color: var(--gray-900); +} + +.nav-tab.active { + background: var(--primary-500); + color: white; + box-shadow: var(--shadow); +} + +.tab-icon { + font-size: 1rem; +} + +/* Content */ +.tab-contents { + position: relative; +} + +.tab-content { + display: none; +} + +.tab-content.active { + display: block; + animation: fadeIn 0.3s ease-out; +} + +@keyframes fadeIn { + from { + opacity: 0; + transform: translateY(8px); + } + to { + opacity: 1; + transform: translateY(0); + } +} + +.content-card { + background: white; + border: 1px solid var(--gray-200); + border-radius: var(--radius-xl); + box-shadow: var(--shadow-sm); + overflow: hidden; +} + +.card-header { + padding: var(--space-6); + border-bottom: 1px solid var(--gray-200); + background: var(--gray-50); +} + +.card-header h2 { + font-size: 1.25rem; + font-weight: 600; + color: var(--gray-900); + margin-bottom: var(--space-2); +} + +.card-header p { + color: var(--gray-600); + margin: 0; +} + +/* Forms */ +.form { + padding: var(--space-6); +} + +.form-group { + margin-bottom: var(--space-6); +} + +.form-row { + display: grid; + grid-template-columns: 1fr 1fr; + gap: var(--space-4); +} + +.input-group { + display: flex; + flex-direction: column; +} + +label { + display: block; + font-weight: 500; + color: var(--gray-700); + margin-bottom: var(--space-2); + font-size: 0.875rem; +} + +input, +textarea, +select { + width: 100%; + padding: var(--space-3) var(--space-4); + border: 1px solid var(--gray-300); + border-radius: var(--radius); + font-size: 0.875rem; + color: var(--gray-700); + background: white; + transition: var(--transition); +} + +input:focus, +textarea:focus, +select:focus { + outline: none; + border-color: var(--primary-500); + box-shadow: 0 0 0 3px rgba(14, 165, 233, 0.1); +} + +textarea { + resize: vertical; + min-height: 100px; +} + +select { + cursor: pointer; +} + +.form-actions { + display: flex; + gap: var(--space-3); + justify-content: flex-end; + margin-top: var(--space-6); +} + +/* Buttons */ +.btn { + display: inline-flex; + align-items: center; + gap: var(--space-2); + padding: var(--space-3) var(--space-4); + border: 1px solid transparent; + border-radius: var(--radius); + font-weight: 500; + font-size: 0.875rem; + text-decoration: none; + cursor: pointer; + transition: var(--transition); + white-space: nowrap; +} + +.btn:disabled { + opacity: 0.6; + cursor: not-allowed; +} + +.btn-primary { + background: var(--primary-500); + color: white; + border-color: var(--primary-500); +} + +.btn-primary:hover:not(:disabled) { + background: var(--primary-600); + border-color: var(--primary-600); + transform: translateY(-1px); + box-shadow: var(--shadow); +} + +.btn-secondary { + background: white; + color: var(--gray-700); + border-color: var(--gray-300); +} + +.btn-secondary:hover:not(:disabled) { + background: var(--gray-50); + border-color: var(--gray-400); +} + +.btn-icon { + font-size: 1rem; +} + +/* Results */ +.result-container { + margin-top: var(--space-6); + display: none; +} + +.result-container.show { + display: block; + animation: fadeIn 0.3s ease-out; +} + +.result-card { + border: 1px solid var(--gray-200); + border-radius: var(--radius-lg); + overflow: hidden; +} + +.result-header { + display: flex; + align-items: center; + gap: var(--space-2); + padding: var(--space-4); + font-weight: 600; + font-size: 0.875rem; +} + +.result-header.success { + background: var(--success-50); + color: var(--success-600); + border-bottom: 1px solid var(--success-100); +} + +.result-header.error { + background: var(--error-50); + color: var(--error-600); + border-bottom: 1px solid var(--error-100); +} + +.result-content { + padding: var(--space-4); + background: white; +} + +.result-json { + background: var(--gray-50); + border: 1px solid var(--gray-200); + border-radius: var(--radius); + padding: var(--space-4); + font-family: "Monaco", "Menlo", "Ubuntu Mono", monospace; + font-size: 0.8rem; + line-height: 1.5; + white-space: pre-wrap; + overflow-x: auto; + color: var(--gray-700); +} + +/* Badges et entités */ +.badge { + display: inline-flex; + align-items: center; + padding: var(--space-1) var(--space-2); + border-radius: var(--radius-sm); + font-size: 0.75rem; + font-weight: 500; + margin: var(--space-1); +} + +.badge.positive { + background: var(--success-100); + color: var(--success-600); +} + +.badge.negative { + background: var(--error-100); + color: var(--error-600); +} + +.badge.neutral { + background: var(--gray-100); + color: var(--gray-600); +} + +.entity { + display: inline-flex; + align-items: center; + padding: var(--space-1) var(--space-2); + border-radius: var(--radius-sm); + font-size: 0.75rem; + font-weight: 500; + margin: var(--space-1); +} + +.entity.person { + background: rgba(59, 130, 246, 0.1); + color: #1d4ed8; +} + +.entity.org { + background: rgba(245, 158, 11, 0.1); + color: #b45309; +} + +.entity.loc { + background: rgba(34, 197, 94, 0.1); + color: #15803d; +} + +.entity.misc { + background: rgba(168, 85, 247, 0.1); + color: #7c3aed; +} + +/* Batch results */ +.batch-results { + display: grid; + gap: var(--space-4); +} + +.batch-item { + background: var(--gray-50); + border: 1px solid var(--gray-200); + border-radius: var(--radius); + padding: var(--space-4); +} + +.batch-item-header { + font-weight: 500; + color: var(--gray-900); + margin-bottom: var(--space-2); + font-size: 0.875rem; +} + +/* Loading */ +.loading { + display: flex; + flex-direction: column; + align-items: center; + padding: var(--space-8); + color: var(--gray-500); +} + +.spinner { + width: 32px; + height: 32px; + border: 3px solid var(--gray-200); + border-top: 3px solid var(--primary-500); + border-radius: 50%; + animation: spin 1s linear infinite; + margin-bottom: var(--space-4); +} + +@keyframes spin { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(360deg); + } +} + +/* Responsive */ +@media (max-width: 768px) { + .container { + padding: 0 var(--space-3); + } + + .header-content { + flex-direction: column; + gap: var(--space-4); + text-align: center; + } + + .nav-tabs { + flex-wrap: wrap; + } + + .nav-tab { + flex: none; + min-width: auto; + } + + .form-row { + grid-template-columns: 1fr; + } + + .config-form { + flex-direction: column; + } + + .form-actions { + flex-direction: column-reverse; + } + + .btn { + justify-content: center; + } +} + +@media (max-width: 480px) { + .container { + padding: 0 var(--space-2); + } + + .main { + padding: var(--space-4) 0; + } + + .config-card, + .content-card .form, + .card-header { + padding: var(--space-4); + } +} + +/* Styles pour les résultats améliorés */ +.result-header { + display: flex; + justify-content: space-between; + align-items: flex-start; + padding: var(--space-4); + border-bottom: 1px solid var(--gray-200); +} + +.result-header.success { + background: linear-gradient(135deg, var(--success-50) 0%, var(--success-100) 100%); + color: var(--success-800); +} + +.result-header.error { + background: linear-gradient(135deg, var(--error-50) 0%, var(--error-100) 100%); + color: var(--error-800); +} + +.result-header-main { + display: flex; + align-items: center; + gap: var(--space-3); +} + +.result-icon { + font-size: 1.25rem; +} + +.result-title .title { + font-weight: 600; + font-size: 1rem; + display: block; +} + +.result-title .timestamp { + font-size: 0.75rem; + opacity: 0.7; + display: block; + margin-top: 2px; +} + +.result-actions { + display: flex; + gap: var(--space-1); +} + +.result-content { + padding: var(--space-4); +} + +/* Sentiment result styles */ +.sentiment-result { + display: flex; + flex-direction: column; + gap: var(--space-4); +} + +.sentiment-main { + display: flex; + flex-direction: column; + gap: var(--space-3); +} + +.sentiment-label { + display: flex; + align-items: center; + gap: var(--space-3); +} + +.label-title { + font-weight: 600; + color: var(--gray-700); +} + +.confidence-section { + display: flex; + flex-direction: column; + gap: var(--space-2); +} + +.confidence-header { + display: flex; + justify-content: space-between; + align-items: center; +} + +.confidence-title { + font-weight: 600; + color: var(--gray-700); + font-size: 0.875rem; +} + +.confidence-value { + font-weight: 700; + color: var(--primary-600); +} + +.confidence-bar { + background: var(--gray-200); + height: 8px; + border-radius: 4px; + overflow: hidden; +} + +.confidence-progress { + height: 100%; + border-radius: 4px; + transition: width 0.6s ease; +} + +.interpretation { + background: var(--gray-50); + padding: var(--space-3); + border-radius: var(--radius); + border-left: 3px solid var(--primary-500); + font-style: italic; + color: var(--gray-700); +} + +/* NER result styles */ +.ner-result { + display: flex; + flex-direction: column; + gap: var(--space-4); +} + +.entities-container { + display: flex; + flex-wrap: wrap; + gap: var(--space-2); + margin-top: var(--space-2); +} + +.entity-item { + display: flex; + align-items: center; + gap: var(--space-2); + background: var(--gray-50); + padding: var(--space-2) var(--space-3); + border-radius: var(--radius); + border: 1px solid var(--gray-200); +} + +.entity { + font-weight: 600; + padding: var(--space-1) var(--space-2); + border-radius: var(--radius-sm); + font-size: 0.875rem; +} + +.entity.person { + background: var(--primary-100); + color: var(--primary-800); +} +.entity.per { + background: var(--primary-100); + color: var(--primary-800); +} +.entity.organization { + background: var(--warning-100); + color: var(--warning-800); +} +.entity.org { + background: var(--warning-100); + color: var(--warning-800); +} +.entity.location { + background: var(--success-100); + color: var(--success-800); +} +.entity.loc { + background: var(--success-100); + color: var(--success-800); +} +.entity.misc { + background: var(--gray-100); + color: var(--gray-800); +} + +.entity-label { + font-size: 0.75rem; + color: var(--gray-600); + font-weight: 500; +} + +.entity-confidence { + font-size: 0.75rem; + color: var(--gray-500); +} + +.entities-stats { + background: var(--gray-50); + padding: var(--space-3); + border-radius: var(--radius); +} + +.stats-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(120px, 1fr)); + gap: var(--space-2); + margin-top: var(--space-2); +} + +.stat-item { + display: flex; + justify-content: space-between; + align-items: center; + padding: var(--space-2); + background: white; + border-radius: var(--radius-sm); + font-size: 0.875rem; +} + +.stat-type { + font-weight: 500; + color: var(--gray-700); +} + +.stat-count { + font-weight: 700; + color: var(--primary-600); +} + +.no-entities { + text-align: center; + color: var(--gray-500); + font-style: italic; + padding: var(--space-4); + background: var(--gray-50); + border-radius: var(--radius); +} + +/* QA result styles */ +.qa-result { + display: flex; + flex-direction: column; + gap: var(--space-4); +} + +.qa-question, +.qa-answer { + background: var(--gray-50); + padding: var(--space-3); + border-radius: var(--radius); +} + +.qa-question h4, +.qa-answer h4 { + margin-bottom: var(--space-2); + color: var(--gray-700); + font-size: 0.875rem; + font-weight: 600; +} + +.question-text { + color: var(--gray-800); + font-weight: 500; +} + +.answer-text { + color: var(--gray-800); + font-weight: 500; + background: white; + padding: var(--space-3); + border-radius: var(--radius-sm); + border-left: 3px solid var(--primary-500); +} + +.qa-confidence { + display: flex; + flex-direction: column; + gap: var(--space-2); +} + +/* Fillmask result styles */ +.fillmask-result { + display: flex; + flex-direction: column; + gap: var(--space-4); +} + +.predictions-container { + display: flex; + flex-direction: column; + gap: var(--space-2); + margin-top: var(--space-2); +} + +.prediction-item { + display: flex; + align-items: center; + gap: var(--space-3); + padding: var(--space-3); + background: var(--gray-50); + border-radius: var(--radius); + border: 1px solid var(--gray-200); +} + +.prediction-item.rank-first { + background: linear-gradient(135deg, var(--success-50) 0%, var(--success-100) 100%); + border-color: var(--success-200); +} + +.prediction-item.rank-second { + background: linear-gradient(135deg, var(--warning-50) 0%, var(--warning-100) 100%); + border-color: var(--warning-200); +} + +.prediction-rank { + font-weight: 700; + color: var(--primary-600); + font-size: 0.875rem; + min-width: 24px; +} + +.prediction-content { + flex: 1; + display: flex; + justify-content: space-between; + align-items: center; +} + +.prediction-token { + font-weight: 600; + color: var(--gray-800); +} + +.prediction-score { + font-weight: 500; + color: var(--gray-600); + font-size: 0.875rem; +} + +.prediction-bar { + flex: 1; + background: var(--gray-200); + height: 4px; + border-radius: 2px; + margin-left: var(--space-3); + overflow: hidden; +} + +.prediction-progress { + height: 100%; + background: var(--primary-500); + border-radius: 2px; + transition: width 0.6s ease; +} + +.no-predictions { + text-align: center; + color: var(--gray-500); + font-style: italic; + padding: var(--space-4); + background: var(--gray-50); + border-radius: var(--radius); +} + +/* Moderation result styles */ +.moderation-result { + display: flex; + flex-direction: column; + gap: var(--space-4); +} + +.moderation-status { + display: flex; + align-items: center; + gap: var(--space-3); + padding: var(--space-4); + border-radius: var(--radius); + font-weight: 600; +} + +.moderation-status.positive { + background: linear-gradient(135deg, var(--success-50) 0%, var(--success-100) 100%); + color: var(--success-800); + border: 1px solid var(--success-200); +} + +.moderation-status.negative { + background: linear-gradient(135deg, var(--error-50) 0%, var(--error-100) 100%); + color: var(--error-800); + border: 1px solid var(--error-200); +} + +.status-icon { + font-size: 1.25rem; +} + +.moderation-details { + background: var(--gray-50); + padding: var(--space-3); + border-radius: var(--radius); +} + +.moderation-categories { + display: flex; + flex-direction: column; + gap: var(--space-2); + margin-top: var(--space-2); +} + +.category-item { + display: flex; + justify-content: space-between; + align-items: center; + padding: var(--space-2); + background: white; + border-radius: var(--radius-sm); + font-size: 0.875rem; +} + +.category-label { + font-weight: 500; + color: var(--gray-700); +} + +.category-value { + font-weight: 600; + color: var(--gray-800); +} + +/* Text generation result styles */ +.textgen-result { + display: flex; + flex-direction: column; + gap: var(--space-4); +} + +.textgen-prompt { + background: var(--gray-50); + padding: var(--space-3); + border-radius: var(--radius); +} + +.textgen-prompt h4 { + margin-bottom: var(--space-2); + color: var(--gray-700); + font-size: 0.875rem; + font-weight: 600; +} + +.prompt-text { + color: var(--gray-800); + font-style: italic; +} + +.textgen-output { + background: var(--primary-50); + padding: var(--space-3); + border-radius: var(--radius); + border: 1px solid var(--primary-200); +} + +.textgen-output h4 { + margin-bottom: var(--space-2); + color: var(--primary-700); + font-size: 0.875rem; + font-weight: 600; +} + +.generated-text { + background: white; + padding: var(--space-4); + border-radius: var(--radius-sm); + border-left: 3px solid var(--primary-500); +} + +.text-content { + color: var(--gray-800); + line-height: 1.7; + margin-bottom: var(--space-3); +} + +.text-actions { + display: flex; + gap: var(--space-2); + justify-content: flex-end; +} + +/* Error result styles */ +.error-content { + display: flex; + flex-direction: column; + gap: var(--space-3); +} + +.error-message { + background: var(--error-50); + padding: var(--space-3); + border-radius: var(--radius); + border-left: 3px solid var(--error-500); +} + +.error-message strong { + color: var(--error-700); + display: block; + margin-bottom: var(--space-1); +} + +.error-message p { + color: var(--error-800); + margin: 0; +} + +.error-suggestions { + background: var(--warning-50); + padding: var(--space-3); + border-radius: var(--radius); + border-left: 3px solid var(--warning-500); +} + +.error-suggestions h4 { + color: var(--warning-700); + margin-bottom: var(--space-2); + font-size: 0.875rem; +} + +.error-suggestions ul { + margin: 0; + padding-left: var(--space-4); +} + +.error-suggestions li { + color: var(--warning-800); + margin-bottom: var(--space-1); + font-size: 0.875rem; +} + +/* Détails techniques */ +.result-details { + margin-top: var(--space-3); + border: 1px solid var(--gray-200); + border-radius: var(--radius); +} + +.result-details summary { + padding: var(--space-3); + background: var(--gray-50); + cursor: pointer; + font-weight: 500; + color: var(--gray-700); + font-size: 0.875rem; + border-radius: var(--radius); +} + +.result-details summary:hover { + background: var(--gray-100); +} + +.result-details[open] summary { + border-bottom: 1px solid var(--gray-200); + border-radius: var(--radius) var(--radius) 0 0; +} + +.result-json { + padding: var(--space-3); + background: var(--gray-900); + color: var(--gray-100); + font-family: "Courier New", monospace; + font-size: 0.75rem; + white-space: pre-wrap; + overflow-x: auto; + border-radius: 0 0 var(--radius) var(--radius); +} + +/* Loading states */ +.btn.loading { + position: relative; + color: transparent !important; +} + +.btn.loading::after { + content: ""; + position: absolute; + top: 50%; + left: 50%; + width: 16px; + height: 16px; + margin-top: -8px; + margin-left: -8px; + border: 2px solid transparent; + border-top: 2px solid currentColor; + border-radius: 50%; + animation: spin 1s linear infinite; +} + +@keyframes spin { + to { + transform: rotate(360deg); + } +}