Update app.py

app.py

@@ -1,42 +1,41 @@
-
 import streamlit as st
+# Import from the correct package
 from langchain_google_genai import ChatGoogleGenerativeAI  
 from langchain.tools import Tool
-from langchain_community.tools.google_search import GoogleSearchResults
-import json
+from langchain.agents import initialize_agent
+from langchain.agents import AgentType
+from langchain.tools import DuckDuckGoSearchRun
 import os
 
-# Configure LangChain LLM with Gemini 2.0
-llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash-exp", google_api_key=os.getenv("gemini_api"))
 
-# # Google Search Tool (does not require API key)
-# google_search = GoogleSearchResults()
-# search_tool = Tool(
-#     name="Google Search",
-#     func=google_search.run,
-#     description="Search the web for information.",
-# )
+# 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"
-    google_search = GoogleSearchResults(query=query)
-
-    search_tool = Tool(
-        name="Google Search",
-        func=google_search.run,
-        description="Search the web for information about certifications."
-    )
-    # Perform Google Search
-    search_results = search_tool.run(query)
-    
-    # Generate Q&A using Gemini
-    prompt = (
-        f"Based on the following curriculum details, generate key questions and answers in markdown format for the {cert_name} certification exam:\n\n{search_results}"
-    )
+    search_results = ddgs.run(query).split("\n")
+    prompt = f"Based on the following curriculum details, generate key questions and answers in markdown format for the {cert_name} certification exam. Do not include any metadata or unnecessary text, only return the formatted Q&A:\n{search_results}"
     response = llm.invoke(prompt)
     
     try:
         response_text = response.get("content", "No response generated.") if isinstance(response, dict) else response
+        response_text = "\n".join([line for line in response_text.split("\n") if not line.lower().startswith("content=") and "metadata" not in line.lower()])
     except Exception as e:
         response_text = f"Error processing response: {str(e)}"
     
@@ -82,9 +81,11 @@ if st.button("Get Certification Details"):
         links, qa_content = azure_cert_bot(cert_name)
         
         st.markdown("<div class='subheader'>Certification Links & Curriculum</div>", unsafe_allow_html=True)
-        st.markdown(f"<div class='markdown-text-container'>{links}</div>", unsafe_allow_html=True)
+        for link in links:
+            if link.strip():
+                st.markdown(f"<div class='markdown-text-container'>- <a href='{link}' target='_blank'>{link}</a></div>", unsafe_allow_html=True)
         
         st.markdown("<div class='subheader'>Exam Questions & Answers</div>", unsafe_allow_html=True)
         st.markdown(f"<div class='markdown-text-container'>{qa_content}</div>", unsafe_allow_html=True)
     else:
-        st.warning("Please enter a certification name.")
+        st.warning("Please enter a certification name.")