app.py

import gradio as gr
import requests
import json
import os
import shutil
import threading
import mimetypes
from models import OptimizeRequest, AutotuneRequest, QARequest
from api import start_api

threading.Thread(target=start_api, daemon=True).start()

# Base URL for internal calls
BASE_INTERNAL = "http://127.0.0.1:8000"


def call_api(endpoint: str, payload: dict) -> str:
    try:
        r = requests.post(f"{BASE_INTERNAL}{endpoint}", json=payload, timeout=120)
        return json.dumps(r.json(), indent=2)
    except Exception as e:
        return str(e)


def upload_docs_tool(files, docs_path="data/docs"):
    """
    Upload documents to the server's docs folder.

    Accepts local file paths or URLs. Returns a list of MCP FileData-like dicts.
    """
    os.makedirs(docs_path, exist_ok=True)
    saved = []

    for f in files:
        if f.startswith("http://") or f.startswith("https://"):
            # Download file from URL
            r = requests.get(f, stream=True)
            fname = f.split("/")[-1]
            dest = os.path.join(docs_path, fname)
            with open(dest, "wb") as out_file:
                shutil.copyfileobj(r.raw, out_file)
        else:
            # Local file path
            fname = os.path.basename(f)
            dest = os.path.join(docs_path, fname)
            shutil.copy(f, dest)

        mime_type = mimetypes.guess_type(dest)[0] or "application/octet-stream"
        saved.append({
            "path": dest,
            "url": f"file://{os.path.abspath(dest)}",
            "orig_name": fname,
            "mime_type": mime_type,
            "is_stream": False,
            "meta": {}
        })

    return {"status": "ok", "uploaded_files": saved, "docs_path": docs_path}


def optimize_rag_tool(payload: str) -> str:
    """🔧 Explicit optimization request: user provides all pipeline configs manually."""
    return call_api("/optimize_rag", json.loads(payload))


def autotune_tool(payload: str) -> str:
    """🔧 Autotune RAG: recommends chunk sizes and embedding models automatically."""
    return call_api("/autotune_rag", json.loads(payload))


def generate_qa_tool(payload: str) -> str:
    """🧩 Generates a validation QA dataset for RAG evaluation."""
    return call_api("/generate_validation_qa", json.loads(payload))


# Dynamically assign Pydantic model docstrings to MCP tool functions
optimize_rag_tool.__doc__ = OptimizeRequest.__doc__
autotune_tool.__doc__ = AutotuneRequest.__doc__
generate_qa_tool.__doc__ = QARequest.__doc__


def model_to_json(model_cls) -> str:
    return json.dumps({k: v.default for k, v in model_cls.__fields__.items()}, indent=2)


# Default inputs
DEFAULT_UPLOAD_PATH = "data/docs"
DEFAULT_OPTIMIZE_JSON = model_to_json(OptimizeRequest)
DEFAULT_AUTOTUNE_JSON = model_to_json(AutotuneRequest)
DEFAULT_QA_JSON = model_to_json(QARequest)

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# Ragmint MCP Client")

    # Upload Documents
    with gr.Column():
        gr.Markdown("## Upload Documents")
        gr.Markdown("📂 Upload files (local paths or URLs) to your `data/docs` folder")
        upload_files = gr.File(file_count="multiple", type="filepath")
        upload_path = gr.Textbox(value=DEFAULT_UPLOAD_PATH, label="Docs Path")
        upload_btn = gr.Button("Upload", variant="primary")
        upload_out = gr.JSON(label="Response")
        upload_btn.click(upload_docs_tool, inputs=[upload_files, upload_path], outputs=upload_out)
        gr.Markdown("---")

    # Optimize RAG
    with gr.Column():
        gr.Markdown("## Optimize RAG")
        gr.Markdown(OptimizeRequest.__doc__ or "No description available.")
        optimize_input = gr.Textbox(lines=12, value=DEFAULT_OPTIMIZE_JSON, label="OptimizeRequest JSON")
        optimize_btn = gr.Button("Submit", variant="primary")
        optimize_out = gr.Textbox(lines=15, label="Response")
        optimize_btn.click(optimize_rag_tool, inputs=optimize_input, outputs=optimize_out)
        gr.Markdown("---")

    # Autotune RAG
    with gr.Column():
        gr.Markdown("## Autotune RAG")
        gr.Markdown(AutotuneRequest.__doc__ or "No description available.")
        autotune_input = gr.Textbox(lines=12, value=DEFAULT_AUTOTUNE_JSON, label="AutotuneRequest JSON")
        autotune_btn = gr.Button("Submit", variant="primary")
        autotune_out = gr.Textbox(lines=15)
        autotune_btn.click(autotune_tool, inputs=autotune_input, outputs=autotune_out)
        gr.Markdown("---")

    # Generate QA
    with gr.Column():
        gr.Markdown("## Generate QA")
        gr.Markdown(QARequest.__doc__ or "No description available.")
        qa_input = gr.Textbox(lines=12, value=DEFAULT_QA_JSON, label="QARequest JSON")
        qa_btn = gr.Button("Submit", variant="primary")
        qa_out = gr.Textbox(lines=15, label="Response")
        qa_btn.click(generate_qa_tool, inputs=qa_input, outputs=qa_out)
        gr.Markdown("---")

if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        mcp_server=True,
        show_error=True
    )