Delete src/tests/app.py

src/tests/app.py

@@ -1,94 +0,0 @@
-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 config import API_TITLE, API_VERSION, API_DESCRIPTION
-from 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)