app.py

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



# Start FastAPI server in background
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 clear_cache_tool(docs_path="data/docs"):
    """
    🧹 Clear Cache MCP Tool
    Deletes all files and directories inside docs_path on the server.
    Accepts:
    - local paths (str), default='data/docs/'
    """
    try:
        r = requests.post(
            f"{BASE_INTERNAL}/clear_cache",
            data={"docs_path": docs_path},
            timeout=60
        )
        r.raise_for_status()
        return r.json()
    except Exception as e:
        return {"error": str(e)}


def upload_docs_tool(files, docs_path="data/docs"):
    """
    Upload documents to the server's docs folder via FastAPI /upload_docs.
    Accepts:
    - local file paths (str)
    - URLs (str)
    - file-like objects
    """
    import shutil, tempfile

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

    temp_files = []

    try:
        for f in files:
            if isinstance(f, str) and f.startswith(("http://", "https://")):
                # Download URL to a temp file (txt aware)
                resp = requests.get(f, timeout=60)
                resp.raise_for_status()

                # create temp file with proper extension
                ext = os.path.splitext(f)[1] or ".txt"
                tmp = tempfile.NamedTemporaryFile(delete=False, suffix=ext)

                if "text" in resp.headers.get("Content-Type", "").lower():
                    tmp.write(resp.text.encode("utf-8"))
                else:
                    tmp.write(resp.content)

                tmp.close()
                temp_files.append(tmp.name)

                files_payload.append(("files", open(tmp.name, "rb")))

            elif isinstance(f, str):
                # Local file path
                files_payload.append(("files", open(f, "rb")))

            else:
                # File-like object
                files_payload.append(("files", f))

        resp = requests.post(
            f"{BASE_INTERNAL}/upload_docs",
            files=files_payload,
            data={"docs_path": docs_path}
        )
        resp.raise_for_status()
        return resp.json()

    finally:
        # Close all file handles
        for _, file_obj in files_payload:
            if not file_obj.closed:
                file_obj.close()
        # Clean up temp files
        for tmp_file in temp_files:
            try:
                os.unlink(tmp_file)
            except Exception:
                pass


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))


# Assign Pydantic docstrings
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.model_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 Server")

    # 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("---")

    # Upload MCP Documents (no file uploader)
    with gr.Column():
        gr.Markdown("## Upload Documents (URLs) via MCP")
        gr.Markdown("📂 Upload files (URLs) to your `data/docs` folder on MCP.")
        upload_mcp_input = gr.Textbox(
            lines=5,
            placeholder='Enter list of URLs (e.g., ["https://example.com/example.txt",...])',
            label="Files (JSON list)"
        )
        upload_mcp_path = gr.Textbox(value=DEFAULT_UPLOAD_PATH, label="Docs Path")
        upload_mcp_out = gr.JSON(label="Response")
        upload_mcp_btn = gr.Button("Upload via MCP", variant="primary")

        # MCP callable function
        def upload_urls_tool(files_json, docs_path):
            """
                Upload documents to the server's docs folder via MCP.
                Accepts:
                - URLs (str)
                """
            import ast
            try:
                files = ast.literal_eval(files_json)
            except Exception:
                return {"error": "Invalid JSON list of files"}
            return upload_docs_tool(files, docs_path)

        upload_mcp_btn.click(
            upload_urls_tool,
            inputs=[upload_mcp_input, upload_mcp_path],
            outputs=upload_mcp_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, label="Response")
        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("---")

    # Clear Cache
    with gr.Column():
        gr.Markdown("## Clear Cache")
        gr.Markdown("🧹 Deletes all files and directories inside docs_path on the server.")
        clear_path = gr.Textbox(value=DEFAULT_UPLOAD_PATH, label="Docs Path to Clear")
        clear_btn = gr.Button("Clear Cache", variant="primary")
        clear_out = gr.JSON(label="Response")
        clear_btn.click(clear_cache_tool, inputs=[clear_path], outputs=clear_out)
        gr.Markdown("---")

if __name__ == "__main__":

    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        mcp_server=True,
        show_error=True
    )