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| from fastapi import FastAPI | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from pydantic import BaseModel | |
| import joblib | |
| import numpy as np | |
| import os | |
| app = FastAPI(title="GitHub Analytics ML Inference") | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| # Load model at startup | |
| MODEL_PATH = os.path.join(os.path.dirname(__file__), "model.pkl") | |
| model = None | |
| try: | |
| model = joblib.load(MODEL_PATH) | |
| print(f"Model loaded successfully from {MODEL_PATH}") | |
| except Exception as e: | |
| print(f"Warning: Could not load model: {e}") | |
| class PredictionInput(BaseModel): | |
| stars: float | |
| forks: float | |
| contributors: float | |
| commit_frequency: float | |
| def health(): | |
| return {"status": "ok", "model_loaded": model is not None} | |
| def predict(input: PredictionInput): | |
| if model is None: | |
| from fastapi import HTTPException | |
| raise HTTPException(status_code=503, detail="Model not loaded. Run ML training pipeline first.") | |
| # Feature order must match train_model.py: stars, forks, contributors_count, commit_frequency | |
| features = np.array([[ | |
| input.stars, | |
| input.forks, | |
| input.contributors, | |
| input.commit_frequency, | |
| ]]) | |
| prediction = model.predict(features)[0] | |
| return {"predicted_stars_next_month": float(prediction)} | |