Update app.py

app.py

@@ -1,5 +1,5 @@
+import logging
 from fastapi import FastAPI, UploadFile, File, HTTPException
-import uvicorn
 import torchaudio
 import torch.nn.functional as F
 import torch
@@ -8,18 +8,39 @@ import onnxruntime as ort
 from huggingface_hub import hf_hub_download
 import os
 
-app = FastAPI(title="Pakistani LID AI Engine (Standalone)")
+# ==========================================
+# 1. Setup Production Logging
+# ==========================================
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[logging.StreamHandler()]
+)
+logger = logging.getLogger("LID_Engine")
 
-print("📥 Checking/Downloading ONNX Model from Hugging Face...")
-# Yeh line model ko cache kar legi, baar baar download nahi karegi
-model_path = hf_hub_download(repo_id="Hammad712/pakistani-lid-v3-sota", filename="pakistani_lid_v3.onnx")
+app = FastAPI(title="Pakistani LID AI Engine (Production)")
 
-print("🚀 Loading ONNX Session for CPU...")
-session = ort.InferenceSession(model_path, providers=['CPUExecutionProvider'])
+# ==========================================
+# 2. Model Initialization (Runs once on startup)
+# ==========================================
+logger.info("Initializing Application...")
+try:
+    logger.info("Checking/Downloading ONNX Model from Hugging Face...")
+    model_path = hf_hub_download(repo_id="Hammad712/pakistani-lid-v3-sota", filename="pakistani_lid_v3.onnx")
+    
+    logger.info("Loading ONNX Session for CPU...")
+    session = ort.InferenceSession(model_path, providers=['CPUExecutionProvider'])
+    logger.info("✅ ONNX Session successfully loaded and ready!")
+except Exception as e:
+    logger.error(f"❌ Failed to load model during startup: {e}")
+    raise e
 
 labels = ("balochi", "english", "pashto", "sindhi", "urdu")
 id2label = {i: label for i, label in enumerate(labels)}
 
+# ==========================================
+# 3. Core Inference Logic
+# ==========================================
 def predict_audio(audio_path):
     waveform, sr = torchaudio.load(audio_path)
     if waveform.shape[0] > 1: waveform = waveform.mean(dim=0, keepdim=True)
@@ -53,21 +74,29 @@ def predict_audio(audio_path):
     pred_id = np.argmax(probs, axis=1)[0]
     return id2label[pred_id], float(probs[0][pred_id])
 
+# ==========================================
+# 4. API Endpoints
+# ==========================================
 @app.post("/predict")
 async def predict_language(file: UploadFile = File(...)):
+    logger.info(f"Received request for file: {file.filename}")
+    
     if not file.filename.endswith(('.wav', '.mp3', '.m4a', '.ogg')):
+        logger.warning(f"Rejected invalid file type: {file.filename}")
         raise HTTPException(status_code=400, detail="Invalid audio format. Please upload wav, mp3, m4a, or ogg.")
 
     temp_audio_path = f"temp_{file.filename}"
     try:
-        # File save karein
+        # Save file
         with open(temp_audio_path, "wb") as buffer:
             buffer.write(await file.read())
 
-        # Prediction lein
+        # Predict
+        logger.info(f"Processing inference for {file.filename}...")
         lang, confidence = predict_audio(temp_audio_path)
+        logger.info(f"✅ Prediction successful: {lang.upper()} ({confidence:.2%})")
         
-        # Temp file delete kar dein
+        # Cleanup
         os.remove(temp_audio_path)
 
         return {
@@ -76,10 +105,7 @@ async def predict_language(file: UploadFile = File(...)):
             "confidence": round(confidence * 100, 2)
         }
     except Exception as e:
+        logger.error(f"❌ Error processing {file.filename}: {str(e)}", exc_info=True)
         if os.path.exists(temp_audio_path):
             os.remove(temp_audio_path)
-        raise HTTPException(status_code=500, detail=str(e))
-
-if __name__ == "__main__":
-    print("✨ Server is LIVE at: http://localhost:8080")
-    uvicorn.run(app, host="0.0.0.0", port=8080)
+        raise HTTPException(status_code=500, detail="Internal Server Error")