import streamlit as st
from crewai import Crew, Agent, Task, Process
from langchain_core.tools import Tool
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.llms import HuggingFaceHub  # Change back to HuggingFaceHub
import datetime
import os

# --- Environment Setup ---
huggingfacehub_api_token = os.environ.get("HUGGINGFACEHUB_API_TOKEN")


# --- Helper Functions ---

def get_date_range():
    """Calculates yesterday's date for the search query."""
    today = datetime.date.today()
    yesterday = today - datetime.timedelta(days=1)
    return yesterday.strftime("%Y-%m-%d")


# --- Agent and Task Definitions ---

def create_ai_news_crew():
    """Creates the CrewAI crew, agents, and tasks."""

    search_tool_instance = DuckDuckGoSearchRun()

    # Wrap the DuckDuckGoSearchRun instance with the Tool class.
    search_tool = Tool(
        name="DuckDuckGo Search",
        func=search_tool_instance.run,
        description="Useful for searching the web for recent AI news articles.",
    )

    # Define Agents
    researcher = Agent(
        role='AI News Researcher',
        goal='Find the most recent and relevant AI news articles from yesterday',
        backstory="""You are a specialized AI research agent 
            focused on finding the most relevant and impactful news articles 
            related to Artificial Intelligence.  You excel at using search 
            tools effectively to find information.""",
        verbose=True,
        allow_delegation=False,
        tools=[search_tool],
        llm=HuggingFaceHub(  # Use HuggingFaceHub
            repo_id="deepseek-ai/DeepSeek-Coder-33B-Instruct", #Specify the model
            model_kwargs={"temperature": 0.5, "max_new_tokens": 1024, "repetition_penalty": 1.2}, # Add model parameters here
            huggingfacehub_api_token=huggingfacehub_api_token,
        )
    )

    summarizer = Agent(
        role='AI News Summarizer',
        goal='Summarize the key news articles and create a concise daily briefing',
        backstory="""You are an expert at taking multiple pieces of information 
            and condensing them into clear, concise, and informative summaries.
            You are writing for a busy executive who needs to stay up-to-date
            on AI developments quickly.""",
        verbose=True,
        allow_delegation=False,
        llm=HuggingFaceHub(  # Use HuggingFaceHub
            repo_id="deepseek-ai/DeepSeek-Coder-33B-Instruct", #Specify model
            model_kwargs={"temperature": 0.2, "max_new_tokens": 1024, "repetition_penalty": 1.2}, #Add model parameters here
            huggingfacehub_api_token=huggingfacehub_api_token,
        )
    )

    # Define Tasks
    yesterday_str = get_date_range()
    research_task = Task(
        description=f"""Find at least 5 relevant news articles about Artificial Intelligence 
            published on {yesterday_str}. Focus on major breakthroughs, 
            industry news, ethical considerations, and new applications of AI.
            Return the titles and URLs of the most important articles.
            """,
        agent=researcher,
        expected_output="A list of titles and URLs of the most important AI news articles from yesterday."
    )

    summarize_task = Task(
        description="""Using the news articles identified, create a daily AI news 
        briefing.  The briefing should be no more than 500 words and should
        cover the 3-5 most important AI news items from yesterday. Include 
        a very brief (1-2 sentence) summary of each item and, if possible, link to the source.
        Format the output using markdown for readability.
        """,
        agent=summarizer,
        expected_output="A concise daily AI news briefing in markdown format, no more than 500 words."
    )

    # Create Crew
    crew = Crew(
        agents=[researcher, summarizer],
        tasks=[research_task, summarize_task],
        verbose=True,
        process=Process.sequential
    )
    return crew


# --- Streamlit App ---

def main():
    """Main function to run the Streamlit app."""

    st.set_page_config(
        page_title="AI Daily News Briefing",
        page_icon="🤖",
        layout="wide"
    )

    st.title("AI Daily News Briefing 🤖")
    st.write("Get a concise summary of the most important AI news from yesterday.")

    if st.button("Generate Briefing"):
        with st.spinner("Generating your daily AI news briefing..."):
            try:
                crew = create_ai_news_crew()
                result = crew.kickoff()
                st.subheader("Your AI News Briefing:")
                st.markdown(result)

            except Exception as e:
                st.error(f"An error occurred: {e}")
                st.error("Please check your API key, and ensure you have set up the environment correctly.")


if __name__ == "__main__":
    if not huggingfacehub_api_token:
        st.error("HUGGINGFACEHUB_API_TOKEN is not set.  Please set it as an environment variable.")
    else:
        main()