"""
This file is the main file for the hackathon.
It contains the Gradio interface for the hackathon as a MCP server.
It exposes the following tools:
- search_knowledge_base_for_context
- research_write_review_topic

"""
from dotenv import load_dotenv
load_dotenv()  # Load environment variables from .env file

import logging

# Configure logging to write to a file instead of stdout/stderr
# This avoids interference with the MCP communication channel
logging.basicConfig(
    filename='hackathon-mcp.log',  # Log to a file instead of stdout/stderr
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

import gradio as gr
from tools.rag_tools import search_groundx_for_rag_context
from tools.multi_agent_workflow_for_research import run_research_workflow

def search_knowledge_base_for_context(query: str) -> str:
    """
    Searches and retrieves relevant context from a knowledge base (GroundX),
    based on the user's query.

     Example queries:
     - "What are the main features of fuel system of SU-35."
     - "What are the combat potential of SU-35."

    Args:
        query: The search query supplied by the user.

    Returns:
        str: Relevant text content that can be used by the LLM to answer the query.
    """
    logger.info(f"Searching document for RAG context {query}")
    response = search_groundx_for_rag_context(query)
    logger.info(f"RAG Response: {response}")
    return response

def research_write_review_topic(query: str) -> str:
    """
    Helps with writing a report with research, writing, and review on any topic.
    Returns a reviewed topic.

    The query is a string that contains the topic to be researched and reviewed.

    Example queries:
    - "Write me a report on the history of the internet."
    - "Write me a report on origin of the universe."
    - "Write me a report on the impact of climate change on polar bears."
    - "Write me a report on the benefits of meditation."
    - "Write me a report on the future of artificial intelligence."
    - "Write me a report on the effects of social media on mental health."

    Args:
        query (str): The query to research, write and review .

    Returns:
        str: A nicely formatted string.
    """
    try:
        logger.info(f"Researching the topic: {query}")
        result = run_research_workflow(query)
        return result or "Research completed, but no content was generated."
    except Exception as e:
        return f"Error: {e}"

with gr.Blocks() as server_info:
    gr.Markdown("""
    # MCP powered RAG and Research

    I present to you a MCP powered RAG and Research.

    RAG Tool uses GroundX service to fetch the knowledge base. The knowledge base is a document that contains information about the SU-35 aircraft, including its features, capabilities, and specifications.
    Please check [this PDF](https://airgroup2000.com/gallery/albums/userpics/32438/SU-35_TM_eng.pdf) to formulate queries on Sukhoi.

    The Research Tool is implemented using multi-agent workflow using LlamaIndex (ResearchAgent, WriteAgent, and ReviewAgent).

    ## Available Tools

    ### search_knowledge_base_for_context
    - **Description**: Searches and retrieves relevant context from a knowledge base based on the user's query.
    - **Example Queries**:
        - "What are the main features of fuel system of SU-35."
        - "What are the combat potential of SU-35."

    ### research_write_review_topic
    - **Description**: Helps with writing a report with research, writing, and review on any topic.
    - **Example Queries**:
        - "Write me a report on the history of the internet."
        - "Write me a report on origin of the universe."
        - "Write me a report on the impact of climate change on polar bears."

    ## How to Use
    - Use the MCP RAG Tool tab above to query the knowledge base.
    - Use the Research Tool tab above to write report on any topic.

    ## Demo Link
    [Link to Demo on Youtube](https://www.youtube.com/mcp-rag-research)
    """)

mcp_rag_tool = gr.Interface(
    fn=search_knowledge_base_for_context,
    inputs=["text"],
    outputs=[gr.Textbox(label="Knowledge Base", max_lines=10)],
    title="MCP RAG Tool",
    description="Searches and retrieves relevant context from a knowledge base"
)

research_tool = gr.Interface(
    fn=research_write_review_topic,
    inputs=["text"],
    outputs=[gr.Textbox(label="Reviewed Topic", max_lines=10)],
    title="Research Tool",
    description="Helps with report writing with research, writing, and review agents on any topic. ",
    concurrency_limit=10
)

named_interfaces = {
    "Project Information": server_info,
    "RAG - Tool": mcp_rag_tool,
    "Research a Topic - Tool": research_tool
}

# Tab names and interfaces
tab_names = list(named_interfaces.keys())
interface_list = list(named_interfaces.values())

mcp_server = gr.TabbedInterface(
    interface_list,
    tab_names=tab_names,
    title="👁 MCP powered RAG and Research 🌍"
)

# Launch the MCP Server
if __name__ == "__main__":
    mcp_server.queue(default_concurrency_limit=10)
    mcp_server.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        debug=False,
        mcp_server=True
    )