Delete static/app (1).py

static/app (1).py

@@ -1,97 +0,0 @@
-import os
-import pickle
-import joblib
-import numpy as np
-import uvicorn
-from fastapi import FastAPI, UploadFile, File
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse
-from extract import extract_features  # Import feature extractor
-
-# Initialize FastAPI app
-app = FastAPI()
-
-# Enable CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Set up static files directory for frontend
-app.mount("/static", StaticFiles(directory="static"), name="static")
-
-# Load trained model, scaler, and feature list
-model_path = "models/gender_model_lr.pkl"
-scaler_path = "models/scaler_gender_model_lr.pkl"
-feature_list_path = "models/feature_list.pkl"
-
-model = joblib.load(model_path)
-scaler = joblib.load(scaler_path)
-with open(feature_list_path, "rb") as f:
-    feature_list = pickle.load(f)
-
-print("✅ Model, Scaler, and Feature List Loaded Successfully!")
-
-
-# Route to serve UI (index.html)
-@app.get("/")
-async def serve_ui():
-    return FileResponse("static/index.html")
-
-
-# Prediction API
-@app.post("/predict")
-async def predict(audio: UploadFile = File(...)):
-    try:
-        # Save uploaded file
-        UPLOAD_FOLDER = "uploads"
-        os.makedirs(UPLOAD_FOLDER, exist_ok=True)
-        filepath = os.path.join(UPLOAD_FOLDER, audio.filename)
-        
-        with open(filepath, "wb") as f:
-            f.write(await audio.read())
-
-        print(f"🟢 Processing file: {audio.filename}")
-
-        # Extract features
-        features = extract_features(filepath)
-        if features is None:
-            return {"error": "Feature extraction failed"}
-        
-        print(f"🟢 Extracted {len(features)} features.")
-
-        # Scale features
-        features_scaled = scaler.transform([features])
-        print("🟢 Features scaled successfully.")
-
-        # Predict gender
-        prediction = model.predict(features_scaled)[0]
-        confidence = model.predict_proba(features_scaled)[0]
-        print("🟢 Prediction completed.")
-
-        # Format response
-        result = {
-            "gender": "Female" if prediction == 1 else "Male",
-            "confidence": float(max(confidence)),
-            "age_group": "Unknown"
-        }
-
-        print(f"✅ Result: {result}")
-
-        # Delete temp file
-        os.remove(filepath)
-
-        return result
-
-    except Exception as e:
-        print(f"❌ Error: {e}")
-        return {"error": str(e)}
-
-
-# Run FastAPI with Uvicorn
-if __name__ == "__main__":
-    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)