README.md

metadata

title: Argument Mining Backend
emoji: 🧠
colorFrom: blue
colorTo: green
sdk: docker
sdk_version: '3.10'
app_file: app.py
pinned: false

🧠 Argument Mining Backend (FastAPI)

Argument Mining Backend is a REST API designed to predict argumentative relations (Support / Attack) between sentences and analyze text using the ABA (Assumption-Based Argumentation) framework. It is intended to be used alongside a frontend application (e.g., Next.js or React).

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🚀 Features

• Endpoint /predict-text : Predict the relation between two manually provided arguments.
• Endpoint /predict-csv : Predict relations from a CSV file containing argument pairs.
• Endpoint /aba-upload : Upload a text file to generate and analyze an ABA+ framework.
• Endpoint /aba-example : Run ABA analysis on predefined example text.
• Endpoint /aba-exemple/{filename} : Analyze ABA with a predefined text file.
• Text preprocessing before feeding data into the model.
• Chargement d’un modèle sauvegardé (model.pkl, .pt, etc.).
• Load saved machine learning models (.pt, .pkl, etc.) for inference.
• Automatic Swagger documentation for easy API exploration.
• CORS middleware for cross-origin requests.

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📦 Installation

Clone the repository

git clone https://github.com/<ton-user>/argument-backend.git
cd argument-backend

Setup environment

Option 1: Using Conda (recommended)

conda env create -f environment.yml
conda activate argument-backend

Option 2: Using Python venv

python -m venv venv
source venv/bin/activate   # Linux / Mac
venv\Scripts\activate      # Windows

pip install -r requirements.txt

▶️ Running Locally

uvicorn app:app --reload --host 0.0.0.0 --port 8000

• API will be available at: http://127.0.0.1:8000
• Swagger UI documentation: http://127.0.0.1:8000/docs

📂 Project Structure

argument-backend/
│── app.py      # FastAPI application entrypoint
│── aba         # ABA framework modules
│── relations   # Argument relation prediction modules
│── models      # Saved ML models (.pth, .pkl, etc.)

⚡Notes

• Designed for seamless integration with a frontend application for visualizing argument graphs.
• Supports batch prediction with CSV files (limited to 100 rows per request).
• ABA+ framework generation supports assumptions, arguments, attacks, and reverse attacks.

🌐 Live Demo

Check the live frontend here: Arguments Visualization