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

import os
import requests
import gradio as gr

def search_knowledge_base_for_context(query: str) -> str:
    """
    Searches and retrieves relevant context from a knowledge base on Sukhoi SU-35,
    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.
    """
    print(f"Searching knowledge base for RAG context via modal labs: {query}")

    data = {"query": query}
    modal_url = os.getenv("MODAL_LABS_HACKATHON_RAG_TOOLS_URL")
    response = requests.post(modal_url, json=data, timeout=600.0)
    
    if response.status_code != 200:
        print(f"Error in modal RAG response: {response.status_code} - {response.text}")
        return "No relevant information found from the knowledge base."

    print(f"modal RAG Response: {response.text}")
    return response.text or "No relevant information found"

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.
    """
    print(f"Researching the topic via modal labs: {query}")

    data = {"query": query}
    modal_url = os.getenv("MODAL_LABS_HACKATHON_RESEARCH_TOOLS_URL")
    response = requests.post(modal_url, json=data, timeout=600.0)
    
    if response.status_code != 200:
        print(f"Error in modal RESEARCH response: {response.status_code} - {response.text}")
        return "Research completed, but no content was generated."

    print(f"modal RESEARCH Response: {response.text}")
    return response.text or "Research completed, but no content was generated."

with gr.Blocks() as server_info:
    gr.Markdown("""
        # 🏃 MCP Powered RAG and Research Topic 🌍

        I present to you an 🏃 MCP-powered RAG and Research Topic 🌍.
        The Tools are hosted and executed on **Modal Labs** platform.

        RAG Tool uses the **GroundX** storage by **eyelevel.ai** 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 a Multi-Agent Workflow using **LlamaIndex**.
        <br>
        The Agents use **Nebius** provided LLM (meta-llama/Meta-Llama-3.1-8B-Instruct).

        ## 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 is 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 a report on any topic.

        ## Watch the Demo Video here
        [Link to Demo on Youtube](https://youtu.be/wvHBqW2ABGg)
    """)

mcp_rag_tool = gr.Interface(
    fn=search_knowledge_base_for_context,
    inputs=["text"],
    outputs=[gr.Textbox(label="Knowledge Base", max_lines=15)],
    title="MCP RAG Tool",
    description="Searches and retrieves relevant context from a knowledge base",
    concurrency_limit=1
)

research_tool = gr.Interface(
    fn=research_write_review_topic,
    inputs=["text"],
    outputs=[gr.Textbox(label="Reviewed Topic", max_lines=15)],
    title="Research Tool",
    description="Helps with report writing with research, writing, and review agents on any topic. ",
    concurrency_limit=1
)

named_interfaces = {
    "Project Information": server_info,
    "Search Knowledge Base": mcp_rag_tool,
    "Research a Topic": 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 Topic 🌍"
)

# Launch the MCP Server
if __name__ == "__main__":
    mcp_server.queue(default_concurrency_limit=1)
    mcp_server.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        debug=False,
        mcp_server=True
    )