app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List
import requests
import os
import google.generativeai as genai
from dotenv import load_dotenv

load_dotenv() 


app = FastAPI()

# Define input schema
class QAInput(BaseModel):
    questions: List[str]
    answers: List[str]
    

# Set your API key
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")

genai.configure(api_key=GEMINI_API_KEY)

def mock_gemini_response(prompt: str) -> str:
    try:
        model = genai.GenerativeModel("gemini-2.0-flash")
        response = model.generate_content(prompt)
        return response.text.strip()
    except Exception as e:
        return f"Error calling Gemini: {str(e)}"

# Endpoint to recommend course
@app.post("/recommend-course")
def recommend_course(data: QAInput):
    # Step 1: Fetch course list from LMS
    url = "https://lmslearn.frappe.cloud/api/resource/LMS Course"
    headers = {
        "Authorization": "token ecef74adb0ffd76:122897a76b48867",
        "Accept": "application/json"
    }

    try:
        response = requests.get(url, headers=headers)
        response.raise_for_status()
    except requests.RequestException as e:
        raise HTTPException(status_code=500, detail=f"Failed to fetch course list: {str(e)}")

    courses = response.json().get("data", [])

    # Step 2: Build prompt for Gemini
    user_input = "\n".join(f"Q: {q}\nA: {a}" for q, a in zip(data.questions, data.answers))
    course_list = "\n".join([f"- {course['course_name']}" for course in courses if 'course_name' in course])
    prompt = f"""
        You are an intelligent course recommender.

        Based on the following Q&A from a user:
        {user_input}

        Here is a list of available courses:
        {course_list}

        Recommend the most suitable course for the user.

        Instructions:
        - Do not return the user's questions or answers.
        - Return only the title of the most suitable course.
        - Do not modify the course titles from the available course list.
        """

    gemini_response = mock_gemini_response(prompt)

    return {"recommendation": gemini_response}