Update app.py

app.py

@@ -6,10 +6,14 @@ import torch
 import tempfile
 import os
 import uvicorn
+import logging
+
+# تنظیم لاگ
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 app = FastAPI()
 
-# اضافه کردن CORS
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -19,44 +23,83 @@ app.add_middleware(
 )
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
+logger.info(f"Loading model on {device}")
 model = whisper.load_model("large-v3", device=device)
+logger.info("Model loaded successfully")
 
 @app.get("/")
 async def root():
-    return {"message": "Whisper API is running"}
+    return {"message": "Whisper API is running", "device": device}
 
 @app.post("/transcribe")
 async def transcribe_audio(file: UploadFile = File(...)):
-    if not file:
-        raise HTTPException(status_code=400, detail="No file provided")
-    
     tmp_file_path = None
+    
     try:
+        logger.info(f"Received file: {file.filename}, size: {file.size}")
+        
+        if not file or not file.filename:
+            raise HTTPException(status_code=400, detail="No valid file provided")
+        
         contents = await file.read()
+        file_size = len(contents)
+        logger.info(f"File read successfully, size: {file_size} bytes")
         
-        if len(contents) > 50 * 1024 * 1024:
+        if file_size > 50 * 1024 * 1024:
             raise HTTPException(status_code=413, detail="File too large")
         
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+        if file_size == 0:
+            raise HTTPException(status_code=400, detail="Empty file")
+        
+        # تشخیص فرمت فایل
+        file_ext = os.path.splitext(file.filename)[1].lower()
+        if not file_ext:
+            file_ext = ".wav"
+        
+        with tempfile.NamedTemporaryFile(delete=False, suffix=file_ext) as tmp_file:
             tmp_file.write(contents)
             tmp_file_path = tmp_file.name
         
+        logger.info(f"Temp file created: {tmp_file_path}")
+        
         result = model.transcribe(
             tmp_file_path, 
             fp16=False if device == "cpu" else True,
-            language="auto",
+            language=None,
             task="transcribe",
-            verbose=False
+            verbose=False,
+            word_timestamps=False
         )
         
-        return JSONResponse({"text": result["text"].strip()})
+        logger.info("Transcription completed")
+        
+        text = result["text"].strip()
+        if not text:
+            return JSONResponse({"text": "متن شناسایی نشد", "warning": "No speech detected"})
+        
+        return JSONResponse({"text": text})
     
     except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+        logger.error(f"Error in transcription: {str(e)}")
+        if "No module named" in str(e):
+            raise HTTPException(status_code=500, detail="Missing required modules")
+        elif "CUDA" in str(e):
+            raise HTTPException(status_code=500, detail="GPU error")
+        elif "FFmpeg" in str(e):
+            raise HTTPException(status_code=500, detail="Audio processing error")
+        else:
+            raise HTTPException(status_code=500, detail=f"Processing error: {str(e)}")
     
     finally:
         if tmp_file_path and os.path.exists(tmp_file_path):
-            os.unlink(tmp_file_path)
+            try:
+                os.unlink(tmp_file_path)
+                logger.info(f"Temp file deleted: {tmp_file_path}")
+            except:
+                pass
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860, timeout_keep_alive=300)
 
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860, timeout_keep_alive=300)