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+# Use an official Python runtime as a parent image
+FROM python:3.10-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    gcc \
+    libcurl4-openssl-dev \
+    libssl-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python dependencies
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code
+COPY . .
+
+# Expose the port FastAPI runs on
+EXPOSE 80
+
+# Run the application with Uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "80"]

app.py

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+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}
+
+
+