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import os
import sqlite3
import streamlit as st
import google.generativeai as genai
from dotenv import load_dotenv
# Load environment variables from .env
load_dotenv()
# Set up the Google API key for Gemini
api_key = os.getenv("GOOGLE_API_KEY")
if api_key is None:
st.error("GOOGLE_API_KEY not found in environment variables. Please check your .env file.")
else:
# Configure Google Generative AI API
genai.configure(api_key=api_key)
# Function to fetch all courses from the SQLite database
def fetch_all_courses():
conn = sqlite3.connect('courses.db')
cur = conn.cursor()
cur.execute("SELECT title, description, price FROM courses")
rows = cur.fetchall()
conn.close()
return rows
# Function to generate a response using Google Generative AI based on user prompt and available courses
def generate_response(prompt, courses):
try:
# Prepare a detailed context prompt for the LLM
course_details = "\n".join(
[f"Title: {course[0]}, Description: {course[1]}, Price: {course[2]}" for course in courses])
genai_prompt = f"""
You are an expert assistant tasked with finding relevant courses based on user queries.
Below are details of available courses:
{course_details}
Based on this information, respond to the user's query in the most relevant way:
{prompt}
"""
# Generate a response using Google Generative AI
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content([genai_prompt, prompt])
return response.text.strip() # Return the natural language response
except Exception as e:
st.error(f"Error generating a response: {e}")
return None
# Streamlit interface
st.set_page_config(page_title="Smart Search for Courses")
st.header("Find Relevant Courses on Analytics Vidhya")
# User prompt input
user_query = st.text_input("Enter your search query (e.g., 'Show me all free courses on machine learning'):")
submit = st.button("Search")
# Fetch all courses from the database
courses = fetch_all_courses()
# If user submits the query
if submit and user_query:
# Generate a response from Google Generative AI
response = generate_response(user_query, courses)
if response:
st.subheader("Search Results:")
st.write(response)
else:
st.write("Could not generate a response. Please try again.")
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