import os
import streamlit as st
from langchain_groq import ChatGroq
from langchain_community.tools import ArxivQueryRun, WikipediaQueryRun, DuckDuckGoSearchRun
from langchain_community.utilities import WikipediaAPIWrapper, ArxivAPIWrapper
from langchain.agents import initialize_agent, AgentType
from langchain.callbacks import StreamlitCallbackHandler
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()  
api_key = os.getenv("GROQ_API_KEY")  # Get the API key from the environment variables

## Arxiv and Wikipedia Tools
arxiv_wrapper = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)
arxiv = ArxivQueryRun(api_wrapper=arxiv_wrapper)

api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=200)
wiki = WikipediaQueryRun(api_wrapper=api_wrapper)

search = DuckDuckGoSearchRun(name="Search")

## NILESH
st.title("🤖 NileAI - Your AI Search Companion")
"""
Welcome to NileSearch, your AI-powered chat agent for real-time web search and insights.
This app uses `StreamlitCallbackHandler` to display the agent's thoughts and actions transparently.
Explore more LangChain 🧠 Streamlit Agent examples at [github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent).
"""

## Sidebar for Settings
# st.sidebar.title("Settings")
# api_key = st.sidebar.text_input("Enter your Groq API key:", type="password")

if "messages" not in st.session_state:
    st.session_state["messages"] = [
        {"role": "assistant", "content": "Hi! How can I assist you today?"}
    ]

for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["content"])

if prompt := st.chat_input(placeholder="What is machine learning?"):
    st.session_state.messages.append({"role": "user", "content": prompt})
    st.chat_message("user").write(prompt)

    llm = ChatGroq(groq_api_key=api_key, model_name="Llama3-8b-8192", streaming=True)
    tools = [search, arxiv, wiki]

    search_agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True)

    with st.chat_message("assistant"):
        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
        response = search_agent.run(st.session_state.messages, callbacks=[st_cb])
        st.session_state.messages.append({'role': 'assistant', 'content': response})
        st.write(response)