app.py

import gradio as gr
import requests
import json
from models import OptimizeRequest, AutotuneRequest, QARequest
import os
import threading
import uvicorn
from api import app as fastapi_app

API_URL = "http://127.0.0.1:7861"

def start_fastapi():
    uvicorn.run(fastapi_app, host="0.0.0.0", port=7861, log_level="info")

# Start FastAPI in a background thread
threading.Thread(target=start_fastapi, daemon=True).start()

# -------------------------------
# Helper to call API
# -------------------------------
def call_api(endpoint: str, payload: dict) -> str:
    try:
        r = requests.post(f"{API_URL}/{endpoint}", json=payload)
        return json.dumps(r.json(), indent=2)
    except Exception as e:
        return str(e)


# -------------------------------
# MCP Tool Wrappers
# -------------------------------
def upload_docs_tool(files, docs_path="data/docs"):

    os.makedirs(docs_path, exist_ok=True)
    uploaded_files = []

    for file_path in files:
        filename = os.path.basename(file_path)
        dest_path = os.path.join(docs_path, filename)
        import shutil
        shutil.copy(file_path, dest_path)
        uploaded_files.append(filename)

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


def optimize_rag_tool(payload: str) -> str:
    return call_api("optimize_rag", json.loads(payload))


def autotune_tool(payload: str) -> str:
    return call_api("autotune_rag", json.loads(payload))


def generate_qa_tool(payload: str) -> str:
    return call_api("generate_validation_qa", json.loads(payload))


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


# Default payloads
DEFAULT_UPLOAD_PATH = "data/docs"
DEFAULT_UPLOAD_FILES = []  # No files by default
DEFAULT_OPTIMIZE_JSON = model_to_json(OptimizeRequest)
DEFAULT_AUTOTUNE_JSON = model_to_json(AutotuneRequest)
DEFAULT_QA_JSON = model_to_json(QARequest)

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

    # Upload files section
    with gr.Column():
        gr.Markdown("## Upload Documents\nUpload files to a folder on the server.")
        upload_files = gr.File(
            file_types=[".txt", ".md", ".pdf"],  # allowed extensions
            file_count="multiple",  # allow multiple files
            type="filepath",  # returns local file path
            label="Drag & Drop Files"
        )
        upload_path = gr.Textbox(value=DEFAULT_UPLOAD_PATH, label="Docs Path")
        upload_btn = gr.Button("Upload",variant='primary')
        upload_output = gr.Textbox(label="Response")
        upload_btn.click(upload_docs_tool, inputs=[upload_files, upload_path], outputs=upload_output)
        gr.Markdown("---")

    # Optimize RAG
    with gr.Column():
        gr.Markdown("## Optimize RAG\nRun full RAG optimization with custom parameters.")
        optimize_input = gr.Textbox(lines=12, value=DEFAULT_OPTIMIZE_JSON, label="OptimizeRequest JSON")
        optimize_btn = gr.Button("Submit",variant='primary')
        optimize_output = gr.Textbox(lines=15, label="Response")
        optimize_btn.click(optimize_rag_tool, inputs=optimize_input, outputs=optimize_output)
        gr.Markdown("---")

    # Autotune RAG
    with gr.Column():
        gr.Markdown("## Autotune RAG\nRun AutoRAG tuner and full optimization.")
        autotune_input = gr.Textbox(lines=12, value=DEFAULT_AUTOTUNE_JSON, label="AutotuneRequest JSON")
        autotune_btn = gr.Button("Submit",variant='primary')
        autotune_output = gr.Textbox(lines=15, label="Response")
        autotune_btn.click(autotune_tool, inputs=autotune_input, outputs=autotune_output)
        gr.Markdown("---")

    # Generate QA
    with gr.Column():
        gr.Markdown("## Generate QA\nGenerate validation QA dataset from documents.")
        qa_input = gr.Textbox(lines=12, value=DEFAULT_QA_JSON, label="QARequest JSON")
        qa_btn = gr.Button("Submit",variant='primary')
        qa_output = gr.Textbox(lines=15, label="Response")
        qa_btn.click(generate_qa_tool, inputs=qa_input, outputs=qa_output)
        gr.Markdown("---")

demo.launch(mcp_server=True)