app.py

import streamlit as st
# Import from the correct package
from langchain_google_genai import ChatGoogleGenerativeAI  
from langchain.tools import Tool
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.tools import DuckDuckGoSearchRun
import os


# Configure LangChain LLM with Gemini
llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", google_api_key=os.getenv("gemini_api"))

# Use DuckDuckGo Search (no API key needed)
ddgs = DuckDuckGoSearchRun()
search_tool = Tool(
    name="Web Search",
    func=ddgs.run,
    description="Searches the web for relevant certification information."
)

# Create LangChain agent
agent = initialize_agent(
    tools=[search_tool],
    llm=llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)

def azure_cert_bot(cert_name):
    query = f"Microsoft Azure {cert_name} certification curriculum site:microsoft.com"
    search_results = ddgs.run(query).split("\n")
    prompt = f"Based on the following curriculum details, generate key questions and answers for the {cert_name} certification exam:\n{search_results}"
    response = llm.invoke(prompt)
    
    return search_results, response

# Streamlit UI
st.set_page_config(page_title="Azure Certification Prep Assistant", layout="wide")
st.title("Azure Certification Prep Assistant")

cert_name = st.text_input("Enter Azure Certification Name (e.g., AZ-900)", "")
if st.button("Get Certification Details"):
    if cert_name:
        links, qa_content = azure_cert_bot(cert_name)
        
        st.subheader("Certification Links & Curriculum")
        for link in links:
            st.markdown(f"- [{link}]({link})")
        
        st.subheader("Exam Questions & Answers")
        st.write(qa_content)
    else:
        st.warning("Please enter a certification name.")