Create server.py

server.py

@@ -0,0 +1,61 @@
+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+import requests
+import openai  # or use llama API
+import os
+from pydantic import BaseModel
+
+app = FastAPI()
+
+# Allow Gradio frontend access
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load environment variables
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+class FeedbackRequest(BaseModel):
+    message: str
+
+@app.post("/auto_feedback")
+async def auto_feedback(data: FeedbackRequest):
+    user_message = data.message
+
+    # ---- Step 1: Generate AI response (Llama/OpenAI) ----
+    ai_prompt = f"User feedback: {user_message}\nGive short reflection + improvement suggestion."
+    try:
+        completion = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": ai_prompt}]
+        )
+        ai_text = completion.choices[0].message["content"]
+    except Exception as e:
+        ai_text = f"Error generating feedback: {e}"
+
+    # ---- Step 2: Send data to Pulse Survey API ----
+    pulse_payload = {
+        "feedback_text": user_message,
+        "ai_feedback": ai_text,
+        "source": "HuggingFace_Chatbot"
+    }
+    try:
+        pulse_response = requests.post(
+            "https://your-pulse-survey-api.com/api/save_feedback",
+            json=pulse_payload,
+            timeout=10
+        )
+        pulse_status = pulse_response.status_code
+    except Exception as e:
+        pulse_status = f"Failed to send: {e}"
+
+    # ---- Step 3: Return structured response ----
+    return {
+        "user_feedback": user_message,
+        "ai_response": ai_text,
+        "pulse_status": pulse_status
+    }