Create main.py

main.py

@@ -0,0 +1,64 @@
+import os
+import shutil
+import uvicorn
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from kid_coach_pipeline import KidCoachEngine
+
+# Initialize App
+app = FastAPI(title="Kid-Speech Coach API")
+
+# Allow your future Flutter app to access this server
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Global Engine Variable
+engine = None
+
+
+@app.on_event("startup")
+async def startup_event():
+    global engine
+    print("🚀 Server starting... Loading Whisper Model...")
+    # Initialize Engine (Using 'base' for speed, or 'small'/'medium' based on your GPU)
+    engine = KidCoachEngine(model_size="base")
+    print("✅ Kid Coach Engine Ready!")
+
+@app.post("/coach")
+async def coach_audio(file: UploadFile = File(...)):
+    """
+    Endpoint: Accepts audio file, returns coaching analytics JSON.
+    """
+    if not engine:
+        raise HTTPException(status_code=500, detail="Engine not ready")
+
+    # Save uploaded file temporarily
+    temp_filename = f"temp_{file.filename}"
+    try:
+        with open(temp_filename, "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+
+        # Run Analysis
+        result = engine.analyze_audio(temp_filename)
+        
+        # Check for logic errors passed back from the engine
+        if "error" in result:
+             raise HTTPException(status_code=400, detail=result["error"])
+             
+        return result
+
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=str(e))
+    finally:
+        # Cleanup
+        if os.path.exists(temp_filename):
+            os.remove(temp_filename)
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)