Update app.py

app.py

@@ -1,25 +1,68 @@
-import gradio as gr
+from fastapi import FastAPI, Request
+import os
 import requests
+from pydantic import BaseModel
+from dotenv import load_dotenv
+import openai
 
-API_URL = "https://your-fastapi-url.onrender.com/auto_feedback"  # change to your FastAPI deployment URL
+# ✅ Load Hugging Face injected secrets
+load_dotenv()
 
-def get_feedback(user_input):
-    payload = {"message": user_input}
+# FastAPI app
+app = FastAPI()
+
+# Read secrets from environment
+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
+openai.api_key = OPENAI_API_KEY
+
+# Pydantic model for chatbot message
+class Message(BaseModel):
+    text: str
+
+@app.post("/auto_feedback")
+async def auto_feedback(msg: Message):
     try:
-        res = requests.post(API_URL, json=payload)
-        data = res.json()
-        ai_msg = data.get("ai_response", "No response received.")
-        pulse_status = data.get("pulse_status", "N/A")
-        return f"💬 AI Feedback: {ai_msg}\n📡 Pulse API Status: {pulse_status}"
-    except Exception as e:
-        return f"⚠️ Error: {e}"
+        user_input = msg.text
 
-iface = gr.Interface(
-    fn=get_feedback,
-    inputs=gr.Textbox(label="Ask about your survey"),
-    outputs="text",
-    title="Feedback Chatbot",
-    description="Get AI-powered feedback and send it to Pulse Survey API automatically.",
-)
+        # Step 1: Generate AI feedback + recommendation
+        ai_prompt = f"""
+        You are an HR feedback assistant. 
+        A user said: "{user_input}"
+        Generate:
+        1. A short professional feedback (1-2 sentences)
+        2. A practical recommendation for improvement.
+        Return as JSON with keys: 'feedback' and 'recommendation'.
+        """
 
-iface.launch()
+        completion = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "system", "content": ai_prompt}]
+        )
+
+        ai_text = completion.choices[0].message["content"]
+
+        # Step 2: 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,
+            },
+            timeout=10
+        )
+
+        # Step 3: Return structured result to chatbot
+        return {
+            "status": "success",
+            "user_input": user_input,
+            "ai_response": ai_text,
+            "pulse_status": pulse_response.status_code,
+        }
+
+    except Exception as e:
+        return {"status": "error", "message": str(e)}