Update app.py

app.py

@@ -5,35 +5,39 @@ from pydantic import BaseModel
 from dotenv import load_dotenv
 import openai
 
-# ✅ Load Hugging Face injected secrets
+# ✅ Load environment variables (from Hugging Face secrets)
 load_dotenv()
 
-# FastAPI app
-app = FastAPI()
+# ✅ Initialize FastAPI app
+app = FastAPI(title="AI Feedback Engine")
 
-# Read secrets from environment
+# ✅ Read secrets from environment variables
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 PULSE_API_URL = os.getenv("PULSE_API_URL")
 PULSE_API_KEY = os.getenv("PULSE_API_KEY")
 
-# Configure OpenAI
+# ✅ Configure OpenAI
 openai.api_key = OPENAI_API_KEY
 
-# Pydantic model for chatbot message
+# ✅ Pydantic model for chatbot message
 class Message(BaseModel):
     text: str
 
+@app.get("/")
+def home():
+    return {"message": "🚀 AI Feedback Engine is running!"}
+
 @app.post("/auto_feedback")
 async def auto_feedback(msg: Message):
     try:
         user_input = msg.text
 
-        # Step 1: Generate AI feedback + recommendation
+        # Step 1️⃣: Generate AI feedback + recommendation
         ai_prompt = f"""
-        You are an HR feedback assistant. 
+        You are an HR feedback assistant.
         A user said: "{user_input}"
         Generate:
-        1. A short professional feedback (1-2 sentences)
+        1. A short, professional feedback (1–2 sentences)
         2. A practical recommendation for improvement.
         Return as JSON with keys: 'feedback' and 'recommendation'.
         """
@@ -45,18 +49,15 @@ async def auto_feedback(msg: Message):
 
         ai_text = completion.choices[0].message["content"]
 
-        # Step 2: Send to Pulse Survey API
+        # Step 2️⃣: Send to Pulse Survey API
         pulse_response = requests.post(
             f"{PULSE_API_URL}/pulse-survey-answers/store",
             headers={"Authorization": f"Bearer {PULSE_API_KEY}"},
-            json={
-                "question": user_input,
-                "answer": ai_text,
-            },
+            json={"question": user_input, "answer": ai_text},
             timeout=10
         )
 
-        # Step 3: Return structured result to chatbot
+        # Step 3️⃣: Return structured result to chatbot
         return {
             "status": "success",
             "user_input": user_input,
@@ -66,3 +67,8 @@ async def auto_feedback(msg: Message):
 
     except Exception as e:
         return {"status": "error", "message": str(e)}
+
+# ✅ This part ensures it runs locally too (optional)
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)