Create app.py

app.py

@@ -0,0 +1,94 @@
+import os
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.responses import HTMLResponse
+from fastapi.templating import Jinja2Templates
+from pydantic import BaseModel
+from typing import Dict
+from src.tests.config import API_TITLE, API_VERSION, API_DESCRIPTION
+from src.tests.inference import predictor
+
+app = FastAPI(title=API_TITLE, version=API_VERSION, description=API_DESCRIPTION)
+
+templates = Jinja2Templates(directory="src/templates")
+
+
+class PredictionRequest(BaseModel):
+    features: Dict[str, float]
+
+
+class PredictionResponse(BaseModel):
+    prediction: str
+    features_used: int
+
+
+class ProbabilityResponse(BaseModel):
+    probabilities: Dict[str, float]
+    features_used: int
+
+
+@app.get("/", response_class=HTMLResponse)
+async def home(request: Request):
+    feature_names = predictor.get_feature_names()
+    # Load top 10 features from features.json
+    predictor.load_model()  # Ensure features are loaded
+    top_10_features = predictor.features['top_10_features']
+    return templates.TemplateResponse(
+        "index.html",
+        {"request": request, "features": {"all_features": feature_names, "top_10_features": top_10_features}}
+    )
+
+
+@app.get("/health")
+async def health():
+    return {
+        "status": "healthy",
+        "model_loaded": predictor._model_loaded,
+        "model_ready": predictor.model is not None
+    }
+
+
+@app.get("/features")
+async def get_features():
+    return {"features": predictor.get_feature_names()}
+
+
+@app.post("/predict", response_model=PredictionResponse)
+async def predict(request: PredictionRequest):
+
+    # Validate missing features
+    expected = set(predictor.get_feature_names())
+    incoming = set(request.features.keys())
+
+    missing = expected - incoming
+    if missing:
+        raise HTTPException(400, f"Missing features: {missing}")
+    prediction = predictor.predict(request.features)
+
+    return PredictionResponse(
+        prediction=prediction,
+        features_used=len(request.features)
+    )
+
+
+@app.post("/predict_proba", response_model=ProbabilityResponse)
+async def predict_proba(request: PredictionRequest):
+
+    # Validate missing features
+    expected = set(predictor.get_feature_names())
+    incoming = set(request.features.keys())
+
+    missing = expected - incoming
+    if missing:
+        raise HTTPException(400, f"Missing features: {missing}")
+    probabilities = predictor.predict_proba(request.features)
+
+    return ProbabilityResponse(
+        probabilities=probabilities,
+        features_used=len(request.features)
+    )
+
+
+if __name__ == "__main__":
+    import uvicorn
+    port = int(os.environ.get("PORT", 8000))
+    uvicorn.run(app, host="0.0.0.0", port=port)