Update app.py

app.py

@@ -46,13 +46,14 @@ def get_pipeline():
 
 
 # ✅ AI Function - Processes and ranks workouts
-
 def analyze_workouts(last_workouts: str):
     pipe = get_pipeline()
     if pipe is None:
-        return "❌ AI model is not loaded."
+        print("❌ AI model is not loaded.")
+        return "❌ AI model not loaded."
 
     if not last_workouts.strip():
+        print("❌ Empty workout data received!")
         return "❌ No workout data provided."
 
     instruction = (
@@ -60,7 +61,7 @@ def analyze_workouts(last_workouts: str):
         f"\n\n{last_workouts}\n\nOnly return rankings. No extra text."
     )
 
-    print(f"📨 Sending prompt to AI: {instruction}")  # ✅ Debug log
+    print(f"📨 Sending prompt to AI: {instruction}")
 
     try:
         result = pipe(instruction, max_new_tokens=200, temperature=0.3, top_p=0.9)
@@ -69,11 +70,11 @@ def analyze_workouts(last_workouts: str):
             return "❌ AI did not return a valid response."
 
         response_text = result[0]["generated_text"].strip()
-        print(f"🔍 AI Response: {response_text}")  # ✅ Debug log
+        print(f"🔍 AI Response: {response_text}")
 
         return response_text
     except Exception as e:
-        print(f"❌ AI Error: {str(e)}")  # ✅ Debug AI errors
+        print(f"❌ AI Error: {str(e)}")
         return f"❌ Error: {str(e)}"
 
 
@@ -93,20 +94,13 @@ async def process_workout_request(request: Request):
 
         response_text = analyze_workouts(last_workouts)
 
-        # ✅ Store response for polling fallback
-        event_store[event_id] = response_text  
-
-        # ✅ Send AI response to Vapor Webhook
-        async with httpx.AsyncClient() as client:
-            try:
-                webhook_response = await client.post(WEBHOOK_URL, json={"event_id": event_id, "data": [response_text]})
-                webhook_response.raise_for_status()
-                print(f"✅ Webhook sent successfully: {webhook_response.json()}")
-            except Exception as e:
-                print(f"⚠️ Webhook failed: {e}")
-                print("🔄 Switching to Polling Mode...")
+        if response_text and response_text.strip():
+            event_store[event_id] = response_text  
+            print(f"📝 Stored event: {event_id} → {response_text}")
+        else:
+            print("❌ AI did not generate a valid response. Not storing event.")
 
-        return {"event_id": event_id}  
+        return {"event_id": event_id}
 
     except Exception as e:
         print(f"❌ Error processing request: {e}")
@@ -117,8 +111,13 @@ async def process_workout_request(request: Request):
 @app.get("/gradio_api/poll/{event_id}")
 async def poll(event_id: str):
     """Fetches stored AI response for a given event ID."""
+    print(f"🔍 Polling event ID: {event_id}")
+    
     if event_id in event_store:
+        print(f"✅ Returning stored response: {event_store[event_id]}")
         return {"data": [event_store.pop(event_id)]}
+
+    print("❌ Event ID not found in event_store")
     return {"detail": "Not Found"}
 
 
@@ -128,25 +127,6 @@ async def root():
     return {"message": "Workout Analysis & Ranking AI is running!"}
 
 
-# ✅ Gradio UI for Testing
-iface = gr.Interface(
-    fn=analyze_workouts,
-    inputs="text",
-    outputs="text",
-    title="Workout Analysis & Ranking AI",
-    description="Enter workout data to analyze effectiveness, rank workouts, and receive improvement recommendations."
-)
-
-
-# ✅ Start Both FastAPI & Gradio
-def start_gradio():
-    iface.launch(server_name="0.0.0.0", server_port=7860, share=True)
-
-def start_fastapi():
-    uvicorn.run(app, host="0.0.0.0", port=7861)
-
-# ✅ Run both servers in parallel
+# ✅ Start FastAPI
 if __name__ == "__main__":
-    import threading
-    threading.Thread(target=start_gradio).start()
-    threading.Thread(target=start_fastapi).start()
+    uvicorn.run(app, host="0.0.0.0", port=7861)