Update app.py

app.py

@@ -1,64 +1,22 @@
-from fastapi import FastAPI, Request
+from fastapi import FastAPI
 from pydantic import BaseModel
-from openai import OpenAI
 import requests
-import time
 
 app = FastAPI()
 
-# -----------------------
-# API Keys & Config
-# -----------------------
-OPENROUTER_API_KEY = "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"
-PULSE_BEARER_TOKEN = "3673|1Cg9jkntwA0827JLsmIoUoR4E2hOj2sLkMwEYF8dcdd9ed59"
-COMPANY_ID = "4"
-BASE_URL = "https://pulse-survey.ospreyibs.com/api/v1"
-
-client = OpenAI(
-    base_url="https://openrouter.ai/api/v1",
-    api_key=OPENROUTER_API_KEY
-)
-
-headers = {
-    "Authorization": f"Bearer {PULSE_BEARER_TOKEN}",
-    "Company-Id": COMPANY_ID,
-    "Accept": "application/json",
-    "Content-Type": "application/json"
-}
-
-
-class QuestionRequest(BaseModel):
+class Question(BaseModel):
     question_text: str
 
+@app.get("/")
+def home():
+    return {"message": "🚀 Feedback Engine is running!"}
 
-@app.post("/generate_feedback/")
-async def generate_feedback(request: QuestionRequest):
-    """
-    Endpoint to generate answer + recommendation for a question.
-    """
-    question = request.question_text
-
-    # Generate Answer
-    prompt = f"Answer this question positively: {question}"
-    answer_response = client.chat.completions.create(
-        model="meta-llama/llama-3.3-70b-instruct",
-        messages=[
-            {"role": "system", "content": "You are a helpful AI survey assistant."},
-            {"role": "user", "content": prompt}
-        ]
-    )
-    answer = answer_response.choices[0].message.content.strip()
-
-    # Generate Recommendation
-    recommendation_prompt = f"Based on this answer: {answer}, write one professional recommendation or reflection tip."
-    rec_response = client.chat.completions.create(
-        model="meta-llama/llama-3.3-70b-instruct",
-        messages=[
-            {"role": "user", "content": recommendation_prompt}
-        ]
-    )
-    recommendation = rec_response.choices[0].message.content.strip()
-
+@app.post("/generate-response")
+def generate_response(data: Question):
+    # Dummy response for now
+    answer = f"Llama Response: {data.question_text}"
+    recommendation = "Recommendation: Reflect and take one positive step today."
+    
     return {
         "answer": answer,
         "recommendation": recommendation