Update main.py

main.py

@@ -6,7 +6,7 @@ from fastapi import FastAPI, UploadFile, File, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from kid_coach_pipeline import KidCoachEngine
 
-app = FastAPI(title="Kid-Speech Coach API")
+app = FastAPI(title="Public Speaking Coach API")
 
 app.add_middleware(
     CORSMiddleware,
@@ -15,79 +15,91 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+# Global Engine Instance
 engine = None
 
 @app.on_event("startup")
 async def startup_event():
     global engine
-    # Use 'base' for CPU hosting (Hugging Face Free Tier)
-    print("🚀 Server starting... Loading Whisper Model...")
-    engine = KidCoachEngine(model_size="base")
-    print("✅ Kid Coach Engine Ready!")
+    
+    # 1. Get Token from Secrets
+    hf_token = os.environ.get("HF_TOKEN")
+    if not hf_token:
+        print("❌ CRITICAL: HF_TOKEN not found in environment variables!")
+    
+    print("🚀 Initializing KidCoach Engine (Production Mode)...")
+    try:
+        engine = KidCoachEngine(hf_token=hf_token)
+        print("✅ Engine Ready! Waiting for audio...")
+    except Exception as e:
+        print(f"❌ Engine initialization failed: {e}")
 
 def convert_to_wav(input_path, output_path):
     """
-    Forces conversion to 16kHz Mono WAV using system FFmpeg.
-    This fixes issues with M4A/AAC files from iPhones.
+    Sanitizes audio for AI processing.
+    Converts any input (m4a, mp3, webm) to 16kHz Mono WAV.
     """
     try:
         command = [
             "ffmpeg",
             "-i", input_path,
-            "-ar", "16000",      # 16kHz sample rate
-            "-ac", "1",          # Mono channel
-            "-c:a", "pcm_s16le", # Standard WAV codec
+            "-ar", "16000",      # Standard AI Sample Rate
+            "-ac", "1",          # Mono
+            "-c:a", "pcm_s16le", # Raw WAV
             output_path,
-            "-y"                 # Overwrite if exists
+            "-y"
         ]
-        # Run conversion silently
         subprocess.run(command, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
         return True
     except Exception as e:
-        print(f"❌ FFmpeg conversion failed: {e}")
+        print(f"❌ FFmpeg error: {e}")
         return False
 
 @app.post("/coach")
 async def coach_audio(file: UploadFile = File(...)):
+    global engine
     if not engine:
-        raise HTTPException(status_code=500, detail="Engine not ready")
+        raise HTTPException(status_code=500, detail="AI Engine is not initialized")
 
-    # 1. Save the raw upload (likely .m4a)
+    # 1. Save Raw File
     raw_filename = f"raw_{file.filename}"
     clean_wav_filename = f"clean_{file.filename}.wav"
-    
+
     try:
+        # Write upload to disk
         with open(raw_filename, "wb") as buffer:
             shutil.copyfileobj(file.file, buffer)
 
-        # 2. CONVERT to WAV (The Fix)
-        # This ensures Librosa/Whisper gets a perfect file every time
-        print(f"   🔄 Converting {raw_filename} to WAV...")
+        # 2. Convert to Pristine WAV
+        print(f"🔄 Processing file: {file.filename}")
         success = convert_to_wav(raw_filename, clean_wav_filename)
         
         if not success:
-            raise HTTPException(status_code=400, detail="Audio file corrupted or unreadable")
+            raise HTTPException(status_code=400, detail="Audio file unreadable. Please upload MP3, WAV, or M4A.")
+
+        # 3. Run The Full AI Pipeline
+        # This calls our robust logic in kid_coach_pipeline.py
+        result = engine.process_pipeline(clean_wav_filename)
 
-        # 3. Analyze the CLEAN WAV file
-        result = engine.analyze_audio(clean_wav_filename)
-        
         if "error" in result:
-             # Pass the specific error from the engine back to the app
-             raise HTTPException(status_code=400, detail=result["error"])
-             
+            print(f"Pipeline Error: {result['error']}")
+            raise HTTPException(status_code=500, detail=result["error"])
+
         return result
 
     except HTTPException as he:
         raise he
     except Exception as e:
-        import traceback
-        traceback.print_exc()
-        raise HTTPException(status_code=500, detail="Internal Server Error")
+        print(f"Server Error: {e}")
+        raise HTTPException(status_code=500, detail="Internal Processing Error")
         
     finally:
-        # 4. Cleanup both files
-        if os.path.exists(raw_filename): os.remove(raw_filename)
-        if os.path.exists(clean_wav_filename): os.remove(clean_wav_filename)
+        # 4. Cleanup temp files to save disk space
+        if os.path.exists(raw_filename):
+            os.remove(raw_filename)
+        if os.path.exists(clean_wav_filename):
+            os.remove(clean_wav_filename)
 
 if __name__ == "__main__":
+    # Hugging Face Spaces expects port 7860
     uvicorn.run(app, host="0.0.0.0", port=7860)