import streamlit as st
from phi.agent import Agent
from phi.model.groq import Groq
from phi.tools.duckduckgo import DuckDuckGo
from dotenv import load_dotenv
import time

# Load environment variables
load_dotenv()

# Groq API Key (Replace with your actual API key)
GROQ_API_KEY = "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"

# Web agent to fetch and summarize news
web_agent = Agent(
    name="Web Agent",
    model=Groq(id="llama-3.3-70b-versatile", api_key=GROQ_API_KEY),
    tools=[DuckDuckGo()],  # Use DuckDuckGo for web searches
    instructions=[
        "Search for the latest AI news.",
        "Summarize the main points clearly and concisely.",
        "Always include sources for all information.",
        "Format the response in Markdown for clarity."
    ],
    show_tool_calls=True,  # Enable logging of tool usage
    markdown=True  # Format the output in Markdown
)

# Function to fetch the latest AI news
def fetch_latest_ai_news():
    # Simulating some processing delay
    time.sleep(3)

    query = "Find the latest news about Artificial Intelligence and summarize it."
    response = web_agent.ask(query)
    
    raw_response = str(response)  # Simulating raw data for terminal-like output
    processed_response = response.get('response', 'No summary available.')  # Cleaned summary
    
    return raw_response, processed_response  # Return raw and processed data for side-by-side display

# Streamlit App Interface
def create_streamlit_app():
    st.title("AI News Fetcher")  # Title of the app
    st.markdown("This app fetches and summarizes the latest AI news using AI models.")

    # Add a button for triggering the process
    if st.button("Generate AI News"):
        with st.spinner("Fetching AI news..."):
            # Get the raw and processed AI news
            raw_response, processed_response = fetch_latest_ai_news()

            # Display raw response (Terminal-like view)
            st.subheader("Raw Data")
            st.text_area("Raw Data", value=raw_response, height=300)

            # Display processed (summarized) response
            st.subheader("Processed Data")
            st.markdown(processed_response)

# Running the Streamlit app
if __name__ == "__main__":
    create_streamlit_app()