Update app.py

app.py

@@ -1,143 +1,10 @@
-
-
+import os, dotenv
 import streamlit as st
-import os
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_google_community import GoogleSearchAPIWrapper
-from langchain_community.utilities import GoogleSerperAPIWrapper
-from langchain.tools import DuckDuckGoSearchRun, Tool
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.agents import create_openai_tools_agent, AgentExecutor
-from langgraph.graph import StateGraph, END
-from langchain_core.messages import HumanMessage
-from typing_extensions import TypedDict
-from typing import Annotated, Sequence
-import functools
-import operator
-
-
-# Initialize tools
-llm = ChatOpenAI()
-
-tavily_tool = TavilySearchResults(max_results=5)
-search_google_tool = Tool(
-    name="GoogleSearch",
-    func=GoogleSearchAPIWrapper().run,
-    description="Search information using Google Search API."
-)
-
-duckduck_search_tool = Tool(
-    name="DuckDuckGoSearch",
-    func=DuckDuckGoSearchRun().run,
-    description="Search information using DuckDuckGo."
-)
-
-serper_tool = Tool(
-    name="GoogleSerperSearch",
-    func=GoogleSerperAPIWrapper(max_results=5).run,
-    description="Perform searches using Google Serper API."
-)
-
-tavily_tool_wrapped = Tool(
-    name="TavilySearch",
-    func=tavily_tool.run,
-    description="Retrieve search results from Tavily API."
-)
-
-# Define reusable function for agent creation
-def create_agent(llm: ChatOpenAI, tools: list, system_prompt: str):
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            ("system", system_prompt),
-            MessagesPlaceholder(variable_name="messages"),
-            MessagesPlaceholder(variable_name="agent_scratchpad"),
-        ]
-    )
-    agent = create_openai_tools_agent(llm, tools, prompt)
-    executor = AgentExecutor(agent=agent, tools=tools)
-    return executor
-
-
-# Define agents
-def get_agents():
-    cto_agent = create_agent(
-        llm,
-        [duckduck_search_tool],
-        "You are a CTO name finder. Extract the CTO's name from the provided company data."
-    )
-
-    glassdoor_agent = create_agent(
-        llm,
-        [tavily_tool_wrapped, serper_tool],
-        "You are a Glassdoor review scraper. Retrieve reviews about the given company. "
-        "Consider points like Overall Rating, Compensation, Senior Management, Career Opportunities."
-        "Provide stars for each point."
-        "Always scrap the same data"
-    )
-
-    competitor_agent = create_agent(
-        llm,
-        [tavily_tool_wrapped, serper_tool],
-        "You are a competitor finder. Provide details such as a description of competitors and their primary differences."
-        "Output the results in a table format."
-    )
-
-    information_agent = create_agent(
-        llm,
-        [search_google_tool, duckduck_search_tool, serper_tool],
-        "You are an information collector. Retrieve details such as Website, Sector, Industry, Location, Employees, Founding Year, and LinkedIn URL."
-        "Linkedin URL will be always like this https://www.linkedin.com/company/company_name"
-    )
-
-    return cto_agent, glassdoor_agent, competitor_agent, information_agent
-
-
-# Streamlit App
-def main():
-    st.title("Company Insights API")
-    st.write("Enter a company name to fetch details about its CTO, competitors, Glassdoor reviews, and general information.")
-
-    # Input for company name
-    company_name = st.text_input("Enter company name")
-    run_queries = st.button("Run Queries")
-
-    if run_queries:
-        # Prepare agents
-        cto_agent, glassdoor_agent, competitor_agent, information_agent = get_agents()
-
-        # Queries
-        queries = {
-            "CTO": f"Who is the CTO of {company_name}?",
-            "Glassdoor Reviews": f"What are the Glassdoor reviews of {company_name}?",
-            "Competitors": f"What are the competitors of {company_name}?",
-            "Information": f"Give me all information about {company_name}.",
-        }
-
-        results = {}
-        for query_name, query in queries.items():
-            agent = {
-                "CTO": cto_agent,
-                "Glassdoor Reviews": glassdoor_agent,
-                "Competitors": competitor_agent,
-                "Information": information_agent,
-            }[query_name]
-
-            state = {
-                "messages": [HumanMessage(content=query)]
-            }
-
-            try:
-                response = agent.invoke(state)
-                results[query_name] = response.get("output", "No response")
-            except Exception as e:
-                results[query_name] = f"Error: {e}"
-
-        # Display results
-        for query_name, result in results.items():
-            st.subheader(query_name)
-            st.write(result)
-
-
-if __name__ == "__main__":
-    main()
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_community.utilities import ArxivAPIWrapper, WikipediaAPIWrapper
+from langchain_community.tools import ArxivQueryRun, WikipediaQueryRun, DuckDuckGoSearchRun
+from langchain.agents import create_react_agent
+from langchain.agents import AgentExecutor
+from langchain_community.callbacks.streamlit import StreamlitCallbackHandler
+dotenv.load_dotenv()