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.gitignore

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+# Ignore large model file from main git repo — CI pushes it directly to the HF Space repo
+model.pkl
+__pycache__/
+*.pyc

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install dependencies first for better Docker layer caching
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app code and model
+COPY . .
+
+# Hugging Face Docker Spaces expect the app on port 7860
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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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
+
+
+@app.get("/")
+def health():
+    return {"status": "ok", "model_loaded": model is not None}
+
+
+@app.post("/predict")
+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)}

requirements.txt

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+fastapi
+uvicorn
+scikit-learn
+numpy
+joblib