From 9b2a5497d93cfc5fb74ea1dd2da141cf8a34a3fc Mon Sep 17 00:00:00 2001 From: Cyril Date: Sat, 11 Oct 2025 13:26:06 +0200 Subject: [PATCH] Init commit --- .gitignore | 79 ++ README.md | 314 ++++++++ poetry.lock | 1444 +++++++++++++++++++++++++++++++++++ pyproject.toml | 27 + requirements.txt | 4 + src/__init__.py | 4 + src/cli/__init__.py | 7 + src/cli/base.py | 87 +++ src/cli/display.py | 192 +++++ src/commands/__init__.py | 10 + src/commands/fillmask.py | 84 ++ src/commands/moderation.py | 73 ++ src/commands/ner.py | 137 ++++ src/commands/sentiment.py | 48 ++ src/commands/textgen.py | 95 +++ src/config/__init__.py | 6 + src/config/settings.py | 40 + src/main.py | 38 + src/pipelines/__init__.py | 11 + src/pipelines/fillmask.py | 95 +++ src/pipelines/moderation.py | 174 +++++ src/pipelines/ner.py | 179 +++++ src/pipelines/sentiment.py | 54 ++ src/pipelines/template.py | 59 ++ src/pipelines/textgen.py | 82 ++ 25 files changed, 3343 insertions(+) create mode 100644 .gitignore create mode 100644 README.md create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 requirements.txt create mode 100644 src/__init__.py create mode 100644 src/cli/__init__.py create mode 100644 src/cli/base.py create mode 100644 src/cli/display.py create mode 100644 src/commands/__init__.py create mode 100644 src/commands/fillmask.py create mode 100644 src/commands/moderation.py create mode 100644 src/commands/ner.py create mode 100644 src/commands/sentiment.py create mode 100644 src/commands/textgen.py create mode 100644 src/config/__init__.py create mode 100644 src/config/settings.py create mode 100644 src/main.py create mode 100644 src/pipelines/__init__.py create mode 100644 src/pipelines/fillmask.py create mode 100644 src/pipelines/moderation.py create mode 100644 src/pipelines/ner.py create mode 100644 src/pipelines/sentiment.py create mode 100644 src/pipelines/template.py create mode 100644 src/pipelines/textgen.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..8931b80 --- /dev/null +++ b/.gitignore @@ -0,0 +1,79 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +*.egg-info/ +.installed.cfg +*.egg + +# Virtual environments +venv/ +ENV/ +env/ +.venv/ +.env/ + +# PyInstaller +*.manifest +*.spec + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# VS Code +.vscode/ + +# macOS +.DS_Store + +# Logs +*.log + +# dotenv +.env +.env.* + +# Local settings +local_settings.py + +# System files +Thumbs.db +ehthumbs.db +Desktop.ini \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..11b0d6e --- /dev/null +++ b/README.md @@ -0,0 +1,314 @@ +# ๐Ÿง  AI Lab โ€“ Transformers CLI Playground + +> A **pedagogical and technical project** designed for AI practitioners and students to experiment with Hugging Face Transformers through an **interactive Commandโ€‘Line Interface (CLI)**. +> This playground provides readyโ€‘toโ€‘use NLP pipelines (Sentiment Analysis, Named Entity Recognition, Text Generation, Fillโ€‘Mask, Moderation, etc.) in a modular, extensible, and educational codebase. + +--- + +## ๐Ÿ“š Overview + +The **AI Lab โ€“ 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 lab is intentionally structured to demonstrate **clean software design for ML codebases** โ€” with strict separation between configuration, pipelines, CLI logic, and display formatting. + +--- + +## ๐Ÿ—‚๏ธ Project Structure + +```text +src/ +โ”œโ”€โ”€ __init__.py +โ”œโ”€โ”€ main.py # CLI entry point +โ”‚ +โ”œโ”€โ”€ cli/ +โ”‚ โ”œโ”€โ”€ __init__.py +โ”‚ โ”œโ”€โ”€ base.py # CLICommand base class & interactive shell handler +โ”‚ โ””โ”€โ”€ display.py # Console formatting utilities (tables, colors, results) +โ”‚ +โ”œโ”€โ”€ commands/ # User-facing commands wrapping pipeline logic +โ”‚ โ”œโ”€โ”€ __init__.py +โ”‚ โ”œโ”€โ”€ sentiment.py # Sentiment analysis command +โ”‚ โ”œโ”€โ”€ fillmask.py # Masked token prediction command +โ”‚ โ”œโ”€โ”€ textgen.py # Text generation command +โ”‚ โ”œโ”€โ”€ ner.py # Named Entity Recognition command +โ”‚ โ””โ”€โ”€ moderation.py # Toxicity / content moderation command +โ”‚ +โ”œโ”€โ”€ pipelines/ # Machine learning logic (Hugging Face Transformers) +โ”‚ โ”œโ”€โ”€ __init__.py +โ”‚ โ”œโ”€โ”€ template.py # Blueprint for creating new pipelines +โ”‚ โ”œโ”€โ”€ sentiment.py +โ”‚ โ”œโ”€โ”€ fillmask.py +โ”‚ โ”œโ”€โ”€ textgen.py +โ”‚ โ”œโ”€โ”€ ner.py +โ”‚ โ””โ”€โ”€ moderation.py +โ”‚ +โ””โ”€โ”€ config/ + โ”œโ”€โ”€ __init__.py + โ””โ”€โ”€ settings.py # Global configuration (default models, parameters) +``` + +--- + +## โš™๏ธ Installation + +### ๐Ÿงพ Option 1 โ€“ Using Poetry (Recommended) + +> Poetry is used as the main dependency manager. + +```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**. + +To run the CLI inside the Poetry environment: +```bash +poetry run python src/main.py +``` + +--- + +### ๐Ÿ“ฆ Option 2 โ€“ Using pip and requirements.txt + +If you prefer using `requirements.txt` manually: + +```bash +# 1. Create a virtual environment +python -m venv .venv + +# 2. Activate it +# Linux/macOS +source .venv/bin/activate +# Windows PowerShell +.venv\Scripts\Activate.ps1 + +# 3. Install dependencies +pip install -r requirements.txt +``` + +--- + +## โ–ถ๏ธ Usage + +Once installed, launch the CLI with: + +```bash +python -m src.main +# or, if using Poetry +poetry run python src/main.py +``` + +Youโ€™ll see an interactive menu listing the available commands: + +``` +Welcome to AI Lab - Transformers CLI Playground +Available commands: + โ€ข sentiment โ€“ Analyze the sentiment of a text + โ€ข fillmask โ€“ Predict masked words in a sentence + โ€ข textgen โ€“ Generate text from a prompt + โ€ข ner โ€“ Extract named entities from text + โ€ข moderation โ€“ Detect toxic or unsafe content +``` + +### Example Sessions + +#### ๐Ÿ”น Sentiment Analysis +```text +๐Ÿ’ฌ Enter text: I absolutely love this project! +โ†’ Sentiment: POSITIVE (score: 0.998) +``` + +#### ๐Ÿ”น Fillโ€‘Mask +```text +๐Ÿ’ฌ Enter text: The capital of France is [MASK]. +โ†’ Predictions: + 1) Paris score: 0.87 + 2) Lyon score: 0.04 + 3) London score: 0.02 +``` + +#### ๐Ÿ”น Text Generation +```text +๐Ÿ’ฌ Prompt: Once upon a time +โ†’ Output: Once upon a time there was a young AI learning to code... +``` + +#### ๐Ÿ”น NER (Named Entity Recognition) +```text +๐Ÿ’ฌ Enter text: Elon Musk founded SpaceX in California. +โ†’ Entities: + - Elon Musk (PERSON) + - SpaceX (ORG) + - California (LOC) +``` + +#### ๐Ÿ”น Moderation +```text +๐Ÿ’ฌ Enter text: I hate everything! +โ†’ Result: FLAGGED (toxic content detected) +``` + +--- + +## ๐Ÿง  Architecture Overview + +The internal structure follows a clean **Command โ†” Pipeline โ†” Display** pattern: + +```text + โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” + โ”‚ InteractiveCLI โ”‚ + โ”‚ (src/cli/base.py) โ”‚ + โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + โ”‚ + โ–ผ + โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” + โ”‚ Command Layer โ”‚ โ† e.g. sentiment.py + โ”‚ (user commands) โ”‚ + โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + โ”‚ + โ–ผ + โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” + โ”‚ Pipeline Layer โ”‚ โ† e.g. pipelines/sentiment.py + โ”‚ (ML logic) โ”‚ + โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + โ”‚ + โ–ผ + โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” + โ”‚ Display Layer โ”‚ โ† cli/display.py + โ”‚ (format output) โ”‚ + โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +### Key Concepts + +| 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 Faceโ€™s `transformers.pipeline()` to perform inference. | +| **Display** | Handles clean console rendering (colored output, tables, JSON formatting). | +| **Config** | Centralizes model names, limits, and global constants. | + +--- + +## โš™๏ธ Configuration + +All configuration is centralized in `src/config/settings.py`. + +Example: + +```python +class Config: + DEFAULT_MODELS = { + "sentiment": "distilbert-base-uncased-finetuned-sst-2-english", + "fillmask": "bert-base-uncased", + "textgen": "gpt2", + "ner": "dslim/bert-base-NER", + "moderation":"unitary/toxic-bert" + } + MAX_LENGTH = 512 + BATCH_SIZE = 8 +``` + +You can easily modify model names to experiment with different checkpoints. + +--- + +## ๐Ÿงฉ Extending the Playground + +To create a new experiment (e.g., keyword extraction): + +1. **Duplicate** `src/pipelines/template.py` โ†’ `src/pipelines/keywords.py` + Implement the `run()` or `analyze()` logic using a new Hugging Face pipeline. + +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`. + +--- + +## ๐Ÿงช Testing + +You can use `pytest` for lightweight validation: + +```bash +pip install pytest +pytest -q +``` + +Recommended structure: + +``` +tests/ +โ”œโ”€โ”€ test_sentiment.py +โ”œโ”€โ”€ test_textgen.py +โ””โ”€โ”€ ... +``` + +--- + +## ๐Ÿงฐ 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 UTFโ€‘8. | + +--- + +## ๐Ÿงญ Development Guidelines + +- Keep **Command** classes lightweight โ€” 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`. + +--- + +## ๐Ÿงฑ 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 + +--- + +## ๐Ÿชช License + +You can include a standard open-source license such as **MIT** or **Apache 2.0** depending on your use case. + +--- + +## ๐Ÿค Contributing + +This repository is meant as an **educational sandbox** for experimenting with Transformers. +Pull requests are welcome for new models, better CLI UX, or educational improvements. + +--- + +### โœจ Key Takeaways + +- Modular and pedagogical design for training environments +- Clean separation between **I/O**, **ML logic**, and **UX** +- Easily extensible architecture for adding custom pipelines +- Perfect sandbox for students, researchers, and developers to learn modern NLP tools + +--- + +> ๐Ÿงฉ Built for experimentation. 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+[tool.poetry.dependencies] +python = ">=3.12,<3.14" +torch = "^2.0.0" +transformers = "^4.30.0" +tokenizers = "^0.13.0" +numpy = "^1.24.0" +accelerate = "^0.20.0" + +[tool.poetry.scripts] +ai-lab = "src.main:main" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..429621d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +torch>=2.0.0 +transformers>=4.30.0 +tokenizers>=0.13.0 +numpy>=1.24.0 diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..01ecfee --- /dev/null +++ b/src/__init__.py @@ -0,0 +1,4 @@ +""" +AI Lab - Transformers Experimentation +""" +__version__ = "0.1.0" diff --git a/src/cli/__init__.py b/src/cli/__init__.py new file mode 100644 index 0000000..0316e4c --- /dev/null +++ b/src/cli/__init__.py @@ -0,0 +1,7 @@ +""" +CLI utilities for AI Lab +""" +from .base import CLICommand, InteractiveCLI +from .display import DisplayFormatter + +__all__ = ['CLICommand', 'InteractiveCLI', 'DisplayFormatter'] diff --git a/src/cli/base.py b/src/cli/base.py new file mode 100644 index 0000000..53687da --- /dev/null +++ b/src/cli/base.py @@ -0,0 +1,87 @@ +from abc import ABC, abstractmethod +from typing import Dict, Any +from src.config import Config + + +class CLICommand(ABC): + """Base class for CLI commands""" + + @property + @abstractmethod + def name(self) -> str: + """Command name""" + pass + + @property + @abstractmethod + def description(self) -> str: + """Command description""" + pass + + @abstractmethod + def run(self) -> None: + """Execute the command""" + pass + + +class InteractiveCLI: + """Interactive CLI handler""" + + def __init__(self): + self.commands: Dict[str, CLICommand] = {} + + def register_command(self, command: CLICommand): + """Register a new command""" + self.commands[command.name] = command + + def show_menu(self): + """Display available commands""" + print(Config.CLI_BANNER) + print(Config.CLI_SEPARATOR) + print("Available commands:") + for name, cmd in self.commands.items(): + print(f" ๐Ÿ“Œ {name}: {cmd.description}") + print(" ๐Ÿ“Œ quit: Exit application") + print(" ๐Ÿ“Œ help: Show this help") + print("-" * 50) + + def show_help(self): + """Show detailed help""" + print("\n๐Ÿ“š Detailed Help") + print("-" * 30) + print("Navigation:") + print(" - Type a command name to execute it") + print(" - Type 'back' in a command to return to menu") + print(" - Type 'quit' or Ctrl+C to exit") + print("\nAvailable commands:") + for name, cmd in self.commands.items(): + print(f" {name}: {cmd.description}") + + def run(self): + """Run the interactive CLI""" + self.show_menu() + + while True: + try: + choice = input("\n๐Ÿ’ฌ Choose a command: ").strip().lower() + + if choice in ['quit', 'exit', 'q']: + print("๐Ÿ‘‹ Goodbye!") + break + + if choice in ['help', 'h', '?']: + self.show_help() + continue + + if choice in self.commands: + print() # Empty line for readability + self.commands[choice].run() + print() # Empty line after command + else: + print("โŒ Unknown command. Type 'help' to see available commands.") + + except KeyboardInterrupt: + print("\n๐Ÿ‘‹ Stopping program") + break + except Exception as e: + print(f"โŒ Error: {e}") diff --git a/src/cli/display.py b/src/cli/display.py new file mode 100644 index 0000000..5c5676c --- /dev/null +++ b/src/cli/display.py @@ -0,0 +1,192 @@ +from typing import Dict, Any + + +class DisplayFormatter: + """Utility class for formatting display output""" + + @staticmethod + def format_sentiment_result(result: Dict[str, Any]) -> str: + """Format sentiment analysis result for display""" + if "error" in result: + return f"โŒ {result['error']}" + + sentiment = result["sentiment"] + confidence = result["confidence"] + emoji = "๐Ÿ˜Š" if sentiment == "POSITIVE" else "๐Ÿ˜ž" + + return f"{emoji} Sentiment: {sentiment}\n๐Ÿ“Š Confidence: {confidence:.2%}" + + @staticmethod + def show_loading(message: str = "Analysis in progress..."): + """Show loading message""" + print(f"\n๐Ÿ” {message}") + + @staticmethod + def show_warning(message: str): + """Show warning message""" + print(f"โš ๏ธ {message}") + + @staticmethod + def show_error(message: str): + """Show error message""" + print(f"โŒ {message}") + + @staticmethod + def show_success(message: str): + """Show success message""" + print(f"โœ… {message}") + + @staticmethod + def format_fillmask_result(result: Dict[str, Any]) -> str: + """Format fill-mask prediction result for display""" + if "error" in result: + return f"โŒ {result['error']}" + + output = [] + output.append(f"๐Ÿ“ Original: {result['original_text']}") + output.append(f"๐ŸŽญ Masks found: {result['masks_count']}") + output.append("") + + if result['masks_count'] == 1: + # Single mask + output.append("๐Ÿ”ฎ Predictions:") + for i, pred in enumerate(result['predictions'], 1): + confidence_bar = "โ–ˆ" * int(pred['score'] * 10) + output.append(f" {i}. '{pred['token']}' ({pred['score']:.1%}) {confidence_bar}") + output.append(f" โ†’ {pred['sequence']}") + else: + # Multiple masks + for mask_info in result['predictions']: + output.append(f"๐Ÿ”ฎ Mask #{mask_info['mask_position']} predictions:") + for i, pred in enumerate(mask_info['predictions'], 1): + confidence_bar = "โ–ˆ" * int(pred['score'] * 10) + output.append(f" {i}. '{pred['token']}' ({pred['score']:.1%}) {confidence_bar}") + output.append("") + + return "\n".join(output) + + @staticmethod + def format_textgen_result(result: Dict[str, Any]) -> str: + """Format text generation result for display""" + if "error" in result: + return f"โŒ {result['error']}" + + output = [] + output.append(f"๐Ÿ“ Prompt: {result['prompt']}") + output.append(f"โš™๏ธ Parameters: max_length={result['parameters']['max_length']}, " + f"temperature={result['parameters']['temperature']}") + output.append("-" * 50) + + for i, gen in enumerate(result['generations'], 1): + if len(result['generations']) > 1: + output.append(f"๐ŸŽฏ Generation {i}:") + + output.append(f"๐Ÿ“„ Full text: {gen['text']}") + if gen['continuation']: + output.append(f"โœจ Continuation: {gen['continuation']}") + + if i < len(result['generations']): + output.append("-" * 30) + + return "\n".join(output) + + @staticmethod + def format_moderation_result(result: Dict[str, Any]) -> str: + """Format content moderation result for display""" + if "error" in result: + return f"โŒ {result['error']}" + + output = [] + output.append(f"๐Ÿ“ Original: {result['original_text']}") + + if result['is_modified']: + output.append(f"๐Ÿ›ก๏ธ Moderated: {result['moderated_text']}") + output.append(f"โš ๏ธ Status: Content modified ({result['words_replaced']} words replaced)") + status_emoji = "๐Ÿ”ด" + else: + output.append("โœ… Status: Content approved (no modifications needed)") + status_emoji = "๐ŸŸข" + + # Toxicity score bar + score = result['toxic_score'] + score_bar = "โ–ˆ" * int(score * 10) + output.append(f"{status_emoji} Toxicity Score: {score:.1%} {score_bar}") + + return "\n".join(output) + + @staticmethod + def format_ner_result(result: Dict[str, Any]) -> str: + """Format NER result for display""" + if "error" in result: + return f"โŒ {result['error']}" + + output = [] + output.append(f"๐Ÿ“ Original: {result['original_text']}") + output.append(f"โœจ Highlighted: {result['highlighted_text']}") + output.append(f"๐ŸŽฏ Found {result['total_entities']} entities (threshold: {result['confidence_threshold']:.2f})") + + if result['entities']: + output.append("\n๐Ÿ“‹ Detected Entities:") + for entity in result['entities']: + confidence_bar = "โ–ˆ" * int(entity['confidence'] * 10) + output.append(f" {entity['emoji']} {entity['text']} โ†’ {entity['label']} " + f"({entity['confidence']:.1%}) {confidence_bar}") + + if result['entity_stats']: + output.append("\n๐Ÿ“Š Entity Statistics:") + for entity_type, stats in result['entity_stats'].items(): + unique_entities = list(set(stats['entities'])) + emoji = result['entities'][0]['emoji'] if result['entities'] else "๐Ÿท๏ธ" + for ent in result['entities']: + if ent['label'] == entity_type: + emoji = ent['emoji'] + break + + output.append(f" {emoji} {entity_type}: {stats['count']} occurrences") + if len(unique_entities) <= 3: + output.append(f" โ†’ {', '.join(unique_entities)}") + else: + output.append(f" โ†’ {', '.join(unique_entities[:3])}... (+{len(unique_entities)-3} more)") + + return "\n".join(output) + + @staticmethod + def format_ner_analysis(result: Dict[str, Any]) -> str: + """Format comprehensive NER document analysis""" + if "error" in result: + return f"โŒ {result['error']}" + + output = [] + output.append("๐Ÿ“Š Document Analysis Results") + output.append("=" * 50) + + # Document statistics + stats = result['document_stats'] + output.append(f"๐Ÿ“„ Document: {stats['word_count']} words, {stats['char_count']} characters") + output.append(f"๐Ÿ“ Structure: ~{stats['sentence_count']} sentences") + output.append(f"๐ŸŽฏ Entity Density: {stats['entity_density']:.2%} (entities per word)") + + # Most common entity type + if 'most_common_entity_type' in result: + common = result['most_common_entity_type'] + output.append(f"๐Ÿ† Most Common: {common['emoji']} {common['type']} ({common['count']} occurrences)") + + output.append(f"\nโœจ Highlighted Text:") + output.append(result['highlighted_text']) + + if result['entity_stats']: + output.append(f"\n๐Ÿ“ˆ Detailed Statistics:") + for entity_type, stats in result['entity_stats'].items(): + unique_entities = list(set(stats['entities'])) + emoji = "๐Ÿท๏ธ" + for ent in result['entities']: + if ent['label'] == entity_type: + emoji = ent['emoji'] + break + + output.append(f"\n{emoji} {entity_type} ({stats['count']} total):") + for entity in unique_entities: + count = stats['entities'].count(entity) + output.append(f" โ€ข {entity} ({count}x)") + + return "\n".join(output) diff --git a/src/commands/__init__.py b/src/commands/__init__.py new file mode 100644 index 0000000..e8ea5d3 --- /dev/null +++ b/src/commands/__init__.py @@ -0,0 +1,10 @@ +""" +AI Lab commands +""" +from .sentiment import SentimentCommand +from .fillmask import FillMaskCommand +from .textgen import TextGenCommand +from .moderation import ModerationCommand +from .ner import NERCommand + +__all__ = ['SentimentCommand', 'FillMaskCommand', 'TextGenCommand', 'ModerationCommand', 'NERCommand'] diff --git a/src/commands/fillmask.py b/src/commands/fillmask.py new file mode 100644 index 0000000..2fc127e --- /dev/null +++ b/src/commands/fillmask.py @@ -0,0 +1,84 @@ +from src.cli.base import CLICommand +from src.cli.display import DisplayFormatter +from src.pipelines.fillmask import FillMaskAnalyzer + + +class FillMaskCommand(CLICommand): + """Interactive fill-mask prediction command""" + + def __init__(self): + self.analyzer = None + + @property + def name(self) -> str: + return "fillmask" + + @property + def description(self) -> str: + return "Interactive fill-mask token prediction" + + def _initialize_analyzer(self): + """Lazy initialization of the analyzer""" + if self.analyzer is None: + print("๐Ÿ”„ Loading fill-mask model...") + self.analyzer = FillMaskAnalyzer() + DisplayFormatter.show_success("Model loaded!") + + def _show_instructions(self): + """Show usage instructions""" + print("\n๐Ÿ“ Fill-Mask Prediction") + print("Replace words with [MASK] token and get predictions") + print("\nExamples:") + print(" - The weather today is [MASK]") + print(" - I love to [MASK] music") + print(" - Paris is the capital of [MASK]") + print("\nType 'back' to return to main menu") + print("Type 'help' to see these instructions again") + print("-" * 50) + + def _get_top_k(self) -> int: + """Get number of predictions from user""" + while True: + try: + top_k_input = input("๐Ÿ“Š Number of predictions (1-10, default=5): ").strip() + if not top_k_input: + return 5 + + top_k = int(top_k_input) + if 1 <= top_k <= 10: + return top_k + else: + DisplayFormatter.show_warning("Please enter a number between 1 and 10") + except ValueError: + DisplayFormatter.show_warning("Please enter a valid number") + + def run(self): + """Run interactive fill-mask prediction""" + self._initialize_analyzer() + self._show_instructions() + + while True: + text = input("\n๐Ÿ’ฌ Enter text with [MASK]: ").strip() + + if text.lower() in ['back', 'return']: + break + + if text.lower() == 'help': + self._show_instructions() + continue + + if not text: + DisplayFormatter.show_warning("Please enter some text") + continue + + if "[MASK]" not in text: + DisplayFormatter.show_warning("Text must contain [MASK] token") + continue + + # Get number of predictions + top_k = self._get_top_k() + + DisplayFormatter.show_loading("Predicting tokens...") + result = self.analyzer.predict(text, top_k=top_k) + formatted_result = DisplayFormatter.format_fillmask_result(result) + print(formatted_result) diff --git a/src/commands/moderation.py b/src/commands/moderation.py new file mode 100644 index 0000000..0f27f3c --- /dev/null +++ b/src/commands/moderation.py @@ -0,0 +1,73 @@ +from src.cli.base import CLICommand +from src.cli.display import DisplayFormatter +from src.pipelines.moderation import ContentModerator + + +class ModerationCommand(CLICommand): + """Interactive content moderation command""" + + def __init__(self): + self.moderator = None + + @property + def name(self) -> str: + return "moderation" + + @property + def description(self) -> str: + return "Content moderation and filtering" + + def _initialize_moderator(self): + """Lazy initialization of the moderator""" + if self.moderator is None: + print("๐Ÿ”„ Loading content moderation model...") + self.moderator = ContentModerator() + DisplayFormatter.show_success("Moderation model loaded!") + + def run(self): + """Run interactive content moderation""" + self._initialize_moderator() + + print("\n๐Ÿ›ก๏ธ Content Moderation") + print("Type 'back' to return to main menu") + print("Type 'settings' to adjust moderation sensitivity") + print("-" * 40) + + while True: + text = input("\n๐Ÿ“ Enter text to moderate: ").strip() + + if text.lower() in ['back', 'return']: + break + + if text.lower() == 'settings': + self._show_settings() + continue + + if not text: + DisplayFormatter.show_warning("Please enter some text") + continue + + DisplayFormatter.show_loading("Analyzing content...") + result = self.moderator.moderate(text) + formatted_result = DisplayFormatter.format_moderation_result(result) + print(formatted_result) + + def _show_settings(self): + """Show and allow modification of moderation settings""" + print(f"\nโš™๏ธ Current Settings:") + print(f"Toxicity threshold: {self.moderator.toxicity_threshold:.2f}") + print("\nOptions:") + print("1. Change threshold (0.0 = very strict, 1.0 = very permissive)") + print("2. Back to moderation") + + choice = input("\nChoose option (1-2): ").strip() + + if choice == "1": + try: + new_threshold = float(input("Enter new threshold (0.0-1.0): ")) + self.moderator.set_threshold(new_threshold) + DisplayFormatter.show_success(f"Threshold set to {new_threshold:.2f}") + except ValueError: + DisplayFormatter.show_error("Invalid threshold value") + elif choice != "2": + DisplayFormatter.show_warning("Invalid option") diff --git a/src/commands/ner.py b/src/commands/ner.py new file mode 100644 index 0000000..10a23c5 --- /dev/null +++ b/src/commands/ner.py @@ -0,0 +1,137 @@ +from src.cli.base import CLICommand +from src.cli.display import DisplayFormatter +from src.pipelines.ner import NamedEntityRecognizer + + +class NERCommand(CLICommand): + """Interactive Named Entity Recognition command""" + + def __init__(self): + self.recognizer = None + self.confidence_threshold = 0.9 + + @property + def name(self) -> str: + return "ner" + + @property + def description(self) -> str: + return "Named Entity Recognition - Extract people, places, organizations" + + def _initialize_recognizer(self): + """Lazy initialization of the recognizer""" + if self.recognizer is None: + print("๐Ÿ”„ Loading NER model...") + self.recognizer = NamedEntityRecognizer() + DisplayFormatter.show_success("NER model loaded!") + + def _show_instructions(self): + """Show usage instructions and examples""" + print("\n๐ŸŽฏ Named Entity Recognition") + print("Extract and classify entities like people, organizations, locations, etc.") + print("\n๐Ÿ“ Examples to try:") + print(" - Apple Inc. was founded by Steve Jobs in Cupertino, California.") + print(" - Barack Obama visited Paris in 2015 to meet Emmanuel Macron.") + print(" - Microsoft acquired GitHub for $7.5 billion in June 2018.") + print("\n๐ŸŽ›๏ธ Commands:") + print(" 'back' - Return to main menu") + print(" 'help' - Show these instructions") + print(" 'settings' - Adjust confidence threshold") + print(" 'types' - Show entity types") + print(" 'analyze' - Detailed document analysis mode") + print("-" * 60) + + def _show_entity_types(self): + """Show available entity types""" + entity_types = self.recognizer.get_entity_types() + print("\n๐Ÿท๏ธ Entity Types:") + type_descriptions = { + "PER": "Person names", + "ORG": "Organizations, companies", + "LOC": "Locations, places", + "MISC": "Miscellaneous entities", + "DATE": "Dates and time periods", + "TIME": "Specific times", + "MONEY": "Monetary amounts", + "PERCENT": "Percentages" + } + + for entity_type, emoji in entity_types.items(): + description = type_descriptions.get(entity_type, "Other entities") + print(f" {emoji} {entity_type}: {description}") + + def _adjust_settings(self): + """Allow user to adjust confidence threshold""" + print(f"\nโš™๏ธ Current confidence threshold: {self.confidence_threshold:.2f}") + print("Lower values = more entities detected (but less accurate)") + print("Higher values = fewer entities detected (but more accurate)") + + try: + new_threshold = input(f"Enter new threshold (0.1-1.0, current: {self.confidence_threshold}): ").strip() + if new_threshold: + threshold = float(new_threshold) + if 0.1 <= threshold <= 1.0: + self.confidence_threshold = threshold + DisplayFormatter.show_success(f"Threshold set to {threshold:.2f}") + else: + DisplayFormatter.show_warning("Threshold must be between 0.1 and 1.0") + except ValueError: + DisplayFormatter.show_error("Invalid threshold value") + + def _analyze_mode(self): + """Document analysis mode with detailed statistics""" + print("\n๐Ÿ“Š Document Analysis Mode") + print("Enter longer text for comprehensive entity analysis") + print("Type 'done' when finished") + print("-" * 40) + + lines = [] + while True: + line = input("๐Ÿ“ ").strip() + if line.lower() == 'done': + break + if line: + lines.append(line) + + if not lines: + DisplayFormatter.show_warning("No text entered") + return + + document = " ".join(lines) + DisplayFormatter.show_loading("Analyzing document...") + + result = self.recognizer.analyze_document(document, self.confidence_threshold) + formatted_result = DisplayFormatter.format_ner_analysis(result) + print(formatted_result) + + def run(self): + """Run interactive NER""" + self._initialize_recognizer() + self._show_instructions() + + while True: + text = input("\n๐Ÿ’ฌ Enter text to analyze: ").strip() + + if text.lower() == 'back': + break + elif text.lower() == 'help': + self._show_instructions() + continue + elif text.lower() == 'settings': + self._adjust_settings() + continue + elif text.lower() == 'types': + self._show_entity_types() + continue + elif text.lower() == 'analyze': + self._analyze_mode() + continue + + if not text: + DisplayFormatter.show_warning("Please enter some text") + continue + + DisplayFormatter.show_loading("Extracting entities...") + result = self.recognizer.recognize(text, self.confidence_threshold) + formatted_result = DisplayFormatter.format_ner_result(result) + print(formatted_result) diff --git a/src/commands/sentiment.py b/src/commands/sentiment.py new file mode 100644 index 0000000..4601f73 --- /dev/null +++ b/src/commands/sentiment.py @@ -0,0 +1,48 @@ +from src.cli.base import CLICommand +from src.cli.display import DisplayFormatter +from src.pipelines.sentiment import SentimentAnalyzer + + +class SentimentCommand(CLICommand): + """Interactive sentiment analysis command""" + + def __init__(self): + self.analyzer = None + + @property + def name(self) -> str: + return "sentiment" + + @property + def description(self) -> str: + return "Interactive sentiment analysis" + + def _initialize_analyzer(self): + """Lazy initialization of the analyzer""" + if self.analyzer is None: + print("๐Ÿ”„ Loading sentiment model...") + self.analyzer = SentimentAnalyzer() + DisplayFormatter.show_success("Model loaded!") + + def run(self): + """Run interactive sentiment analysis""" + self._initialize_analyzer() + + print("\n๐Ÿ“ Sentiment Analysis") + print("Type 'back' to return to main menu") + print("-" * 30) + + while True: + text = input("\n๐Ÿ’ฌ Enter your text: ").strip() + + if text.lower() in ['back', 'return']: + break + + if not text: + DisplayFormatter.show_warning("Please enter some text") + continue + + DisplayFormatter.show_loading() + result = self.analyzer.analyze(text) + formatted_result = DisplayFormatter.format_sentiment_result(result) + print(formatted_result) diff --git a/src/commands/textgen.py b/src/commands/textgen.py new file mode 100644 index 0000000..95fe9a2 --- /dev/null +++ b/src/commands/textgen.py @@ -0,0 +1,95 @@ +from src.cli.base import CLICommand +from src.cli.display import DisplayFormatter +from src.pipelines.textgen import TextGenerator + + +class TextGenCommand(CLICommand): + """Interactive text generation command""" + + def __init__(self): + self.generator = None + self.default_params = { + 'max_length': 100, + 'num_return_sequences': 1, + 'temperature': 1.0, + 'do_sample': True + } + + @property + def name(self) -> str: + return "textgen" + + @property + def description(self) -> str: + return "Interactive text generation" + + def _initialize_generator(self): + """Lazy initialization of the generator""" + if self.generator is None: + print("๐Ÿ”„ Loading text generation model...") + self.generator = TextGenerator() + DisplayFormatter.show_success("Model loaded!") + + def _show_parameters(self): + """Show current generation parameters""" + print("\nโš™๏ธ Current parameters:") + for key, value in self.default_params.items(): + print(f" {key}: {value}") + + def _update_parameters(self): + """Allow user to update generation parameters""" + print("\n๐Ÿ”ง Update parameters (press Enter to keep current value):") + + try: + max_length = input(f"Max length ({self.default_params['max_length']}): ").strip() + if max_length: + self.default_params['max_length'] = int(max_length) + + num_sequences = input(f"Number of sequences ({self.default_params['num_return_sequences']}): ").strip() + if num_sequences: + self.default_params['num_return_sequences'] = int(num_sequences) + + temperature = input(f"Temperature ({self.default_params['temperature']}): ").strip() + if temperature: + self.default_params['temperature'] = float(temperature) + + do_sample = input(f"Use sampling ({self.default_params['do_sample']}): ").strip().lower() + if do_sample in ['true', 'false']: + self.default_params['do_sample'] = do_sample == 'true' + + DisplayFormatter.show_success("Parameters updated!") + + except ValueError as e: + DisplayFormatter.show_error(f"Invalid parameter value: {e}") + + def run(self): + """Run interactive text generation""" + self._initialize_generator() + + print("\n๐Ÿ“ Text Generation") + print("Commands:") + print(" 'back' - Return to main menu") + print(" 'params' - Show current parameters") + print(" 'config' - Update parameters") + print("-" * 40) + + while True: + prompt = input("\n๐Ÿ’ฌ Enter your prompt: ").strip() + + if prompt.lower() == 'back': + break + elif prompt.lower() == 'params': + self._show_parameters() + continue + elif prompt.lower() == 'config': + self._update_parameters() + continue + + if not prompt: + DisplayFormatter.show_warning("Please enter a prompt") + continue + + DisplayFormatter.show_loading("Generating text...") + result = self.generator.generate(prompt, **self.default_params) + formatted_result = DisplayFormatter.format_textgen_result(result) + print(formatted_result) diff --git a/src/config/__init__.py b/src/config/__init__.py new file mode 100644 index 0000000..e6a0e4e --- /dev/null +++ b/src/config/__init__.py @@ -0,0 +1,6 @@ +""" +Project configuration +""" +from .settings import Config + +__all__ = ['Config'] diff --git a/src/config/settings.py b/src/config/settings.py new file mode 100644 index 0000000..8d9b2e8 --- /dev/null +++ b/src/config/settings.py @@ -0,0 +1,40 @@ +""" +Global project configuration +""" +from pathlib import Path +from typing import Dict, Any + + +class Config: + """Global application configuration""" + + # Paths + PROJECT_ROOT = Path(__file__).parent.parent.parent + SRC_DIR = PROJECT_ROOT / "src" + + # 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", + } + + # Interface + CLI_BANNER = "๐Ÿค– AI Lab - Transformers Experimentation" + CLI_SEPARATOR = "=" * 50 + + # Performance + MAX_BATCH_SIZE = 32 + DEFAULT_MAX_LENGTH = 512 + + @classmethod + def get_model(cls, pipeline_name: str) -> str: + """Get default model for a pipeline""" + return cls.DEFAULT_MODELS.get(pipeline_name, "") + + @classmethod + def get_all_models(cls) -> Dict[str, str]: + """Get all configured models""" + return cls.DEFAULT_MODELS.copy() diff --git a/src/main.py b/src/main.py new file mode 100644 index 0000000..a9d559e --- /dev/null +++ b/src/main.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 +""" +CLI entry point for AI Lab +""" +import sys +from pathlib import Path + +# Add parent directory to PYTHONPATH +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from src.cli import InteractiveCLI +from src.commands import SentimentCommand, FillMaskCommand, TextGenCommand, ModerationCommand, NERCommand + + +def main(): + """Main CLI function""" + try: + # Create CLI interface + cli = InteractiveCLI() + + # Register available commands + cli.register_command(SentimentCommand()) + cli.register_command(FillMaskCommand()) + cli.register_command(TextGenCommand()) + cli.register_command(ModerationCommand()) + cli.register_command(NERCommand()) + + # Launch interactive interface + cli.run() + + except KeyboardInterrupt: + print("\n๐Ÿ‘‹ Stopping program") + except Exception as e: + print(f"โŒ Error: {e}") + sys.exit(1) + +if __name__ == "__main__": + main() diff --git a/src/pipelines/__init__.py b/src/pipelines/__init__.py new file mode 100644 index 0000000..5ad9ab6 --- /dev/null +++ b/src/pipelines/__init__.py @@ -0,0 +1,11 @@ +""" +Experimentation pipelines with transformers +""" +from .sentiment import SentimentAnalyzer +from .fillmask import FillMaskAnalyzer +from .textgen import TextGenerator +from .moderation import ContentModerator +from .ner import NamedEntityRecognizer +from .template import TemplatePipeline + +__all__ = ['SentimentAnalyzer', 'FillMaskAnalyzer', 'TextGenerator', 'ContentModerator', 'NamedEntityRecognizer', 'TemplatePipeline'] diff --git a/src/pipelines/fillmask.py b/src/pipelines/fillmask.py new file mode 100644 index 0000000..99817f8 --- /dev/null +++ b/src/pipelines/fillmask.py @@ -0,0 +1,95 @@ +from transformers import pipeline +from typing import Dict, List, Optional +from src.config import Config + + +class FillMaskAnalyzer: + """Fill-mask analyzer using transformers""" + + def __init__(self, model_name: Optional[str] = None): + """ + Initialize the fill-mask pipeline + + Args: + model_name: Name of the model to use (optional) + """ + self.model_name = model_name or Config.get_model("fillmask") + print(f"Loading fill-mask model: {self.model_name}") + self.pipeline = pipeline("fill-mask", model=self.model_name) + print("Model loaded successfully!") + + def predict(self, text: str, top_k: int = 5) -> Dict: + """ + Predict masked tokens in text + + Args: + text: Text with [MASK] token(s) to predict + top_k: Number of top predictions to return + + Returns: + Dictionary with predictions and scores + """ + if not text.strip(): + return {"error": "Empty text"} + + if "[MASK]" not in text: + return {"error": "Text must contain [MASK] token"} + + try: + results = self.pipeline(text, top_k=top_k) + + # Handle single mask vs multiple masks + if isinstance(results, list) and isinstance(results[0], list): + # Multiple masks + predictions = [] + for i, mask_results in enumerate(results): + mask_predictions = [ + { + "token": pred["token_str"], + "score": round(pred["score"], 4), + "sequence": pred["sequence"] + } + for pred in mask_results + ] + predictions.append({ + "mask_position": i + 1, + "predictions": mask_predictions + }) + + return { + "original_text": text, + "masks_count": len(results), + "predictions": predictions + } + else: + # Single mask + predictions = [ + { + "token": pred["token_str"], + "score": round(pred["score"], 4), + "sequence": pred["sequence"] + } + for pred in results + ] + + return { + "original_text": text, + "masks_count": 1, + "predictions": predictions + } + + except Exception as e: + return {"error": f"Prediction error: {str(e)}"} + + def predict_batch(self, texts: List[str], top_k: int = 5) -> List[Dict]: + """ + Predict masked tokens for multiple texts + + Args: + texts: List of texts with [MASK] tokens + top_k: Number of top predictions to return + + Returns: + List of prediction results + """ + return [self.predict(text, top_k) for text in texts] diff --git a/src/pipelines/moderation.py b/src/pipelines/moderation.py new file mode 100644 index 0000000..25738eb --- /dev/null +++ b/src/pipelines/moderation.py @@ -0,0 +1,174 @@ +from transformers import pipeline +from typing import Dict, List, Optional +import re +from src.config import Config + + +class ContentModerator: + """Content moderator that detects and replaces inappropriate content""" + + def __init__(self, model_name: Optional[str] = None): + """ + Initialize the content moderation pipeline + + Args: + model_name: Name of the model to use (optional) + """ + self.model_name = model_name or Config.get_model("moderation") + print(f"Loading moderation model: {self.model_name}") + self.classifier = pipeline("text-classification", model=self.model_name) + print("Moderation model loaded successfully!") + + # Threshold for considering content as toxic + self.toxicity_threshold = 0.5 + + def moderate(self, text: str, replacement: str = "***") -> Dict: + """ + Moderate content by detecting and replacing inappropriate words + + Args: + text: Text to moderate + replacement: String to replace inappropriate content with + + Returns: + Dictionary with original text, moderated text, and detection info + """ + if not text.strip(): + return {"error": "Empty text"} + + try: + # First, check overall toxicity + result = self.classifier(text) + + # Handle different model output formats + if isinstance(result, list): + predictions = result + else: + predictions = [result] + + # Find toxicity score + toxic_score = 0.0 + is_toxic = False + + for pred in predictions: + label = pred["label"].upper() + score = pred["score"] + + # Check different possible toxic labels + if label in ["TOXIC", "TOXICITY", "HARMFUL", "1"]: + toxic_score = max(toxic_score, score) + if score > self.toxicity_threshold: + is_toxic = True + elif label in ["NOT_TOXIC", "CLEAN", "0"]: + # For models where high score means NOT toxic + toxic_score = max(toxic_score, 1.0 - score) + if (1.0 - score) > self.toxicity_threshold: + is_toxic = True + + if not is_toxic: + return { + "original_text": text, + "moderated_text": text, + "is_modified": False, + "toxic_score": toxic_score, + "words_replaced": 0 + } + + # If toxic, analyze word by word to find problematic parts + moderated_text, words_replaced = self._moderate_by_words(text, replacement) + + return { + "original_text": text, + "moderated_text": moderated_text, + "is_modified": True, + "toxic_score": toxic_score, + "words_replaced": words_replaced + } + + except Exception as e: + return {"error": f"Moderation error: {str(e)}"} + + def _moderate_by_words(self, text: str, replacement: str) -> tuple[str, int]: + """ + Moderate text by analyzing individual words and phrases + + Args: + text: Original text + replacement: Replacement string + + Returns: + Tuple of (moderated_text, words_replaced_count) + """ + words = text.split() + moderated_words = [] + words_replaced = 0 + + # Check individual words + for word in words: + # Clean word for analysis (remove punctuation) + clean_word = re.sub(r'[^\w]', '', word) + if not clean_word: + moderated_words.append(word) + continue + + try: + word_result = self.classifier(clean_word) + + # Handle different model output formats + if isinstance(word_result, list): + predictions = word_result + else: + predictions = [word_result] + + is_word_toxic = False + for pred in predictions: + label = pred["label"].upper() + score = pred["score"] + + if label in ["TOXIC", "TOXICITY", "HARMFUL", "1"]: + if score > self.toxicity_threshold: + is_word_toxic = True + break + elif label in ["NOT_TOXIC", "CLEAN", "0"]: + if (1.0 - score) > self.toxicity_threshold: + is_word_toxic = True + break + + if is_word_toxic: + # Replace the clean part with asterisks, keep punctuation + moderated_word = re.sub(r'\w+', replacement, word) + moderated_words.append(moderated_word) + words_replaced += 1 + else: + moderated_words.append(word) + + except: + # If analysis fails for a word, keep it as is + moderated_words.append(word) + + return " ".join(moderated_words), words_replaced + + def moderate_batch(self, texts: List[str], replacement: str = "***") -> List[Dict]: + """ + Moderate multiple texts + + Args: + texts: List of texts to moderate + replacement: String to replace inappropriate content with + + Returns: + List of moderation results + """ + return [self.moderate(text, replacement) for text in texts] + + def set_threshold(self, threshold: float): + """ + Set the toxicity threshold + + Args: + threshold: Threshold between 0 and 1 + """ + if 0 <= threshold <= 1: + self.toxicity_threshold = threshold + else: + raise ValueError("Threshold must be between 0 and 1") \ No newline at end of file diff --git a/src/pipelines/ner.py b/src/pipelines/ner.py new file mode 100644 index 0000000..2d331ef --- /dev/null +++ b/src/pipelines/ner.py @@ -0,0 +1,179 @@ +from transformers import pipeline +from typing import Dict, List, Optional, Tuple +from src.config import Config + + +class NamedEntityRecognizer: + """Named Entity Recognition using transformers""" + + def __init__(self, model_name: Optional[str] = None): + """ + Initialize the NER pipeline + + Args: + model_name: Name of the model to use (optional) + """ + self.model_name = model_name or Config.get_model("ner") + print(f"Loading NER model: {self.model_name}") + self.pipeline = pipeline("ner", model=self.model_name, aggregation_strategy="simple") + print("NER model loaded successfully!") + + # Entity type mappings for better display + self.entity_colors = { + "PER": "๐Ÿ‘ค", # Person + "ORG": "๐Ÿข", # Organization + "LOC": "๐Ÿ“", # Location + "MISC": "๐Ÿท๏ธ", # Miscellaneous + "DATE": "๐Ÿ“…", # Date + "TIME": "โฐ", # Time + "MONEY": "๐Ÿ’ฐ", # Money + "PERCENT": "๐Ÿ“Š", # Percentage + } + + def recognize(self, text: str, confidence_threshold: float = 0.9) -> Dict: + """ + Recognize named entities in text + + Args: + text: Text to analyze + confidence_threshold: Minimum confidence score for entities + + Returns: + Dictionary with entities and their information + """ + if not text.strip(): + return {"error": "Empty text"} + + try: + entities = self.pipeline(text) + + # Filter by confidence and process entities + filtered_entities = [] + entity_stats = {} + + for entity in entities: + if entity["score"] >= confidence_threshold: + entity_type = entity["entity_group"] + + processed_entity = { + "text": entity["word"], + "label": entity_type, + "confidence": round(entity["score"], 4), + "start": entity["start"], + "end": entity["end"], + "emoji": self.entity_colors.get(entity_type, "๐Ÿท๏ธ") + } + + filtered_entities.append(processed_entity) + + # Update statistics + 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"]) + + # Create highlighted text + highlighted_text = self._highlight_entities(text, filtered_entities) + + return { + "original_text": text, + "highlighted_text": highlighted_text, + "entities": filtered_entities, + "entity_stats": entity_stats, + "total_entities": len(filtered_entities), + "confidence_threshold": confidence_threshold + } + + except Exception as e: + return {"error": f"NER processing error: {str(e)}"} + + def _highlight_entities(self, text: str, entities: List[Dict]) -> str: + """ + Create highlighted version of text with entity markers + + Args: + text: Original text + entities: List of detected entities + + Returns: + Text with highlighted entities + """ + if not entities: + return text + + # Sort entities by start position (reverse order for replacement) + sorted_entities = sorted(entities, key=lambda x: x["start"], reverse=True) + + highlighted = text + for entity in sorted_entities: + start, end = entity["start"], entity["end"] + entity_text = entity["text"] + emoji = entity["emoji"] + label = entity["label"] + confidence = entity["confidence"] + + # Create highlighted version + highlight = f"{emoji}[{entity_text}]({label}:{confidence:.2f})" + highlighted = highlighted[:start] + highlight + highlighted[end:] + + return highlighted + + def analyze_document(self, text: str, confidence_threshold: float = 0.9) -> Dict: + """ + Perform comprehensive document analysis with entity extraction + + Args: + text: Document text to analyze + confidence_threshold: Minimum confidence for entities + + Returns: + Comprehensive analysis results + """ + result = self.recognize(text, confidence_threshold) + + if "error" in result: + return result + + # Additional analysis + analysis = { + **result, + "document_stats": { + "word_count": len(text.split()), + "char_count": len(text), + "sentence_count": len([s for s in text.split('.') if s.strip()]), + "entity_density": len(result["entities"]) / len(text.split()) if text.split() else 0 + } + } + + # Find most common entity types + if result["entity_stats"]: + most_common_type = max(result["entity_stats"].items(), key=lambda x: x[1]["count"]) + analysis["most_common_entity_type"] = { + "type": most_common_type[0], + "count": most_common_type[1]["count"], + "emoji": self.entity_colors.get(most_common_type[0], "๐Ÿท๏ธ") + } + + return analysis + + def recognize_batch(self, texts: List[str], confidence_threshold: float = 0.9) -> List[Dict]: + """ + Recognize entities in multiple texts + + Args: + texts: List of texts to analyze + confidence_threshold: Minimum confidence for entities + + Returns: + List of NER results + """ + return [self.recognize(text, confidence_threshold) for text in texts] + + def get_entity_types(self) -> Dict[str, str]: + """ + Get available entity types with their emojis + + Returns: + Dictionary mapping entity types to emojis + """ + return self.entity_colors.copy() diff --git a/src/pipelines/sentiment.py b/src/pipelines/sentiment.py new file mode 100644 index 0000000..3bcf806 --- /dev/null +++ b/src/pipelines/sentiment.py @@ -0,0 +1,54 @@ +from transformers import pipeline +from typing import Dict, List, Optional +from src.config import Config + + +class SentimentAnalyzer: + """Sentiment analyzer using transformers""" + + def __init__(self, model_name: Optional[str] = None): + """ + Initialize the sentiment-analysis pipeline + + Args: + model_name: Name of the model to use (optional) + """ + self.model_name = model_name or Config.get_model("sentiment") + print(f"Loading sentiment model: {self.model_name}") + self.pipeline = pipeline("sentiment-analysis", model=self.model_name) + print("Model loaded successfully!") + + def analyze(self, text: str) -> Dict: + """ + Analyze the sentiment of a text + + Args: + text: Text to analyze + + Returns: + Dictionary with label and confidence score + """ + if not text.strip(): + return {"error": "Empty text"} + + try: + result = self.pipeline(text)[0] + return { + "text": text, + "sentiment": result["label"], + "confidence": round(result["score"], 4) + } + except Exception as e: + return {"error": f"Analysis error: {str(e)}"} + + def analyze_batch(self, texts: List[str]) -> List[Dict]: + """ + Analyze the sentiment of multiple texts + + Args: + texts: List of texts to analyze + + Returns: + List of analysis results + """ + return [self.analyze(text) for text in texts] diff --git a/src/pipelines/template.py b/src/pipelines/template.py new file mode 100644 index 0000000..e1916ec --- /dev/null +++ b/src/pipelines/template.py @@ -0,0 +1,59 @@ +""" +Template for creating new pipelines +Copy this file and adapt it according to your needs +""" +from transformers import pipeline +from typing import Dict, List, Optional + + +class TemplatePipeline: + """Template for a new pipeline""" + + def __init__(self, model_name: Optional[str] = None): + """ + Initialize the pipeline + + Args: + model_name: Name of the model to use (optional) + """ + self.model_name = model_name or "distilbert-base-uncased" + print(f"Loading model {self.model_name}...") + + # Replace "text-classification" with your task + self.pipeline = pipeline("text-classification", model=self.model_name) + print("Model loaded successfully!") + + def process(self, text: str) -> Dict: + """ + Process a text + + Args: + text: Text to process + + Returns: + Dictionary with results + """ + if not text.strip(): + return {"error": "Empty text"} + + try: + result = self.pipeline(text) + return { + "text": text, + "result": result, + # Add other fields according to your needs + } + except Exception as e: + return {"error": f"Processing error: {str(e)}"} + + def process_batch(self, texts: List[str]) -> List[Dict]: + """ + Process multiple texts + + Args: + texts: List of texts to process + + Returns: + List of results + """ + return [self.process(text) for text in texts] diff --git a/src/pipelines/textgen.py b/src/pipelines/textgen.py new file mode 100644 index 0000000..8c6b5bf --- /dev/null +++ b/src/pipelines/textgen.py @@ -0,0 +1,82 @@ +from transformers import pipeline +from typing import Dict, List, Optional +from src.config import Config + + +class TextGenerator: + """Text generator using transformers""" + + def __init__(self, model_name: Optional[str] = None): + """ + Initialize the text-generation pipeline + + Args: + model_name: Name of the model to use (optional) + """ + 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) + print("Model loaded successfully!") + + def generate(self, prompt: str, max_length: 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 + num_return_sequences: Number of sequences to generate + temperature: Sampling temperature (higher = more random) + do_sample: Whether to use sampling + + Returns: + Dictionary with generated texts + """ + if not prompt.strip(): + return {"error": "Empty prompt"} + + try: + results = self.pipeline( + prompt, + max_length=max_length, + num_return_sequences=num_return_sequences, + temperature=temperature, + do_sample=do_sample, + pad_token_id=self.pipeline.tokenizer.eos_token_id + ) + + generations = [ + { + "text": result["generated_text"], + "continuation": result["generated_text"][len(prompt):].strip() + } + for result in results + ] + + return { + "prompt": prompt, + "parameters": { + "max_length": max_length, + "num_sequences": num_return_sequences, + "temperature": temperature, + "do_sample": do_sample + }, + "generations": generations + } + + except Exception as e: + return {"error": f"Generation error: {str(e)}"} + + def generate_batch(self, prompts: List[str], **kwargs) -> List[Dict]: + """ + Generate text for multiple prompts + + Args: + prompts: List of input prompts + **kwargs: Generation parameters + + Returns: + List of generation results + """ + return [self.generate(prompt, **kwargs) for prompt in prompts]