Create app.py

app.py

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+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
+from google.colab import userdata # Securely storing API keys
+
+
+# Configure LangChain LLM with Gemini
+llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", google_api_key=GEMINI_API_KEY)
+
+# 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.")