app.py

"""
This module provides a FastAPI-based web server for breast cancer prediction and model training.
It exposes endpoints for training a cancer prediction model asynchronously,
checking training status, and making predictions based on input features.

Endpoints:
- POST /train: Starts model training in the background using a provided data file.
- GET /training_status: Returns the current status of the model training process.
- GET /predict: Predicts cancer diagnosis based on input features
                (radius_mean, texture_mean, symmetry_mean, fractal_dimension_mean).

Dependencies:
- FastAPI for API creation
- Pydantic for request validation
- uvicorn for running the server
- CancerPredictor class for model operations (imported from cancer_predictor.py)

Usage:
Run this module to start the API server. Use the endpoints to train the model and make predictions.
"""

from fastapi import FastAPI, HTTPException, BackgroundTasks
import uvicorn

# Import the CancerPredictor class
from cancer_predictor import CancerPredictor

app = FastAPI()

# Create a global instance of CancerPredictor
predictor = CancerPredictor()

# Global variable to store training status
training_status = "Not started"

def train_model(train_path: str, test_path: str):
    global training_status
    training_status = "In progress"
    try:
        predictor.train(train_path, test_path)
        training_status = "Completed"
    except Exception as e:
        training_status = f"Failed: {str(e)}"

@app.post("/train")
async def train(train_path: str, test_path: str, background_tasks: BackgroundTasks):
    background_tasks.add_task(train_model, train_path, test_path)
    return {"message": "Training started in the background"}

@app.get("/training_status")
async def get_training_status():
    return {"status": training_status}

@app.get("/predict")
async def predict(radius_mean: float, texture_mean: float, symmetry_mean: float, fractal_dimension_mean: float):
    if training_status != "Completed":
        raise HTTPException(status_code=400, detail="Model not trained yet")
    try:
        predicted_diagnosis = predictor.predict(
            radius_mean, texture_mean, symmetry_mean, fractal_dimension_mean)
        return {"diagnosis": str(predicted_diagnosis)}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e)) from e

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)