app.py

import gradio as gr
import requests
import json
import server

API_URL = "http://localhost:8000"

def optimize_rag_tool(payload: str) -> str:
    """Run RAGMint full optimization workflow.

    Args:
        payload: JSON string containing OptimizeRequest parameters.

    Returns:
        JSON result with best config and leaderboard stats.
    """
    r = requests.post(f"{API_URL}/optimize_rag", json=json.loads(payload))
    return json.dumps(r.json(), indent=2)

def autotune_tool(payload: str) -> str:
    """Run AutoRAG tuner to recommend best configs and optimize.

    Args:
        payload: JSON string for AutotuneRequest

    Returns:
        JSON result for tuning and full optimization.
    """
    r = requests.post(f"{API_URL}/autotune_rag", json=json.loads(payload))
    return json.dumps(r.json(), indent=2)

def generate_qa_tool(payload: str) -> str:
    """Generate validation QA set automatically with Gemini or Anthropic.

    Args:
        payload: JSON string for QARequest

    Returns:
        JSON preview of generated dataset
    """
    r = requests.post(f"{API_URL}/generate_validation_qa", json=json.loads(payload))
    return json.dumps(r.json(), indent=2)

demo = gr.Interface(
    fn=optimize_rag_tool,
    inputs=gr.Textbox(lines=12, label="OptimizeRequest JSON"),
    outputs=gr.Textbox(label="Response")
)

demo.launch(mcp_server=True)