Update app.py

app.py

@@ -8,7 +8,7 @@ from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 import uvicorn
 import asyncio
 
-# ✅ Load Hugging Face API Token Securely
+# ✅ Securely Load Hugging Face Token
 HF_TOKEN = os.getenv("HF_TOKEN")
 if not HF_TOKEN:
     raise ValueError("❌ HF_TOKEN not found! Set it in Hugging Face Secrets.")
@@ -19,7 +19,7 @@ event_store = {}  # Store AI responses with event_id
 
 app = FastAPI()
 
-# ✅ Log server restarts
+# ✅ Log server restart
 print("🔄 Restarting Hugging Face AI Model Server...")
 
 # ✅ Load AI Model
@@ -33,18 +33,16 @@ except Exception as e:
     print(f"❌ Error loading model: {e}")
     pipe = None
 
-# ✅ AI Function - Processes and ranks workouts
+
+# ✅ AI Function - Analyzes workout data
 def analyze_workouts(last_workouts: str):
     """Generates AI-based workout rankings based on heart rate recovery."""
     if pipe is None:
-        print("❌ AI model is not loaded.")
-        return "❌ AI model not loaded."
+        return "❌ AI model is not loaded."
 
     if not last_workouts.strip():
-        print("❌ Empty workout data received!")
         return "❌ No workout data provided."
 
-    # Structured instruction for better AI processing
     instruction = (
         "You are a fitness AI assistant. Rank the following workouts based on heart rate recovery after 2 minutes."
         "\n\n### Ranking Rules:"
@@ -59,34 +57,24 @@ def analyze_workouts(last_workouts: str):
         "\n4. Strength Training - HR dip: 18 bpm"
         "\n5. Walking - HR dip: 12 bpm"
         "\n6. Yoga - HR dip: 8 bpm"
-        "\n\n⚠️ **DO NOT repeat input. DO NOT explain. ONLY return the ranking in this format.**"
     )
 
     try:
-        print(f"📨 Sending prompt to AI:\n{instruction}")
-
         result = pipe(
             instruction,
             max_new_tokens=250,
-            temperature=0.3,  # Low randomness for structured responses
+            temperature=0.3,
             top_p=0.9,
             do_sample=True,
             return_full_text=False
         )
 
-        print(f"🔍 RAW HF RESPONSE: {result}")
-
         if not result or not result[0].get("generated_text", "").strip():
-            print("❌ AI Response is empty!")
             return "❌ AI did not generate a valid response."
 
-        response_text = result[0]["generated_text"].strip()
-        print(f"📜 AI Response after cleanup: {response_text}")
-
-        return response_text
+        return result[0]["generated_text"].strip()
 
     except Exception as e:
-        print(f"❌ AI Processing Error: {e}")
         return f"❌ Error generating workout recommendation: {str(e)}"
 
 
@@ -95,36 +83,34 @@ def analyze_workouts(last_workouts: str):
 async def process_workout_request(request: Request):
     try:
         req_body = await request.json()
-        print("📩 RAW REQUEST FROM HF:", req_body)  # Debug log
+        print("📩 RAW REQUEST FROM HF:", req_body)  
 
         if "data" not in req_body or not isinstance(req_body["data"], list):
             raise HTTPException(status_code=400, detail="Invalid request format: 'data' must be a list.")
 
         last_workouts = req_body["data"][0]
-        event_id = str(uuid.uuid4())  # Generate event ID
+        event_id = str(uuid.uuid4())  
 
         print(f"✅ Processing AI Request - Event ID: {event_id}")
 
         response_text = analyze_workouts(last_workouts)
 
-        # Store response for polling
-        event_store[event_id] = response_text
+        event_store[event_id] = response_text  
 
-        # ✅ Send AI response to webhook if provided
         webhook_url = req_body.get("webhook_url")
         if webhook_url:
             print(f"📡 Sending response to Webhook: {webhook_url}")
             async with httpx.AsyncClient() as client:
                 await client.post(webhook_url, json={"event_id": event_id, "data": [response_text]})
 
-        return {"event_id": event_id}  # Return event_id for tracking
+        return {"event_id": event_id}  
 
     except Exception as e:
         print(f"❌ Error processing request: {e}")
         raise HTTPException(status_code=500, detail=str(e))
 
 
-# ✅ Polling Endpoint (If Webhook Fails)
+# ✅ Polling API (If Webhook Fails)
 @app.get("/gradio_api/poll/{event_id}")
 async def poll(event_id: str):
     """Fetches stored AI response for a given event ID."""
@@ -160,6 +146,7 @@ iface = gr.Interface(
     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)