import base64
import io
import gradio as gr
from fastmcp import Client
from fastmcp.client import StreamableHttpTransport
import asyncio
import ast

# -------------------------------
# MCP server info
# -------------------------------
ROBOT_ID = "Robot_MCP_Client"  # Local client identifier
MCP_SERVER_URL = "https://oppaai-robot-mcp-server.hf.space/gradio_api/mcp/"
SERVER_NAME = "Robot_MCP_Server"
TOOL_NAME = "Robot_MCP_Server_robot_watch"

# -------------------------------
# Initialize MCP client globally
# -------------------------------
HTTP_TRANSPORT = StreamableHttpTransport(url=MCP_SERVER_URL)
MCP_CLIENT = Client(transport=HTTP_TRANSPORT, name=SERVER_NAME)


# -------------------------------
# Async function using user's HF token
# -------------------------------
async def process_webcam_stream_async(image, oauth_token: gr.OAuthToken | None):
    """
    Send webcam image to MCP server using user's HF token and process the response.
    """
    if oauth_token is None:
        return "Please log in first.", "", "", "", "", "", "", ""

    if image is None:
        return "", "", "", "", "", "", "", ""

    # Convert image to Base64
    buffered = io.BytesIO()
    image.save(buffered, format="JPEG")
    b64_img = base64.b64encode(buffered.getvalue()).decode("utf-8")

    # Payload with user token
    payload = {
        "hf_token_input": oauth_token.token,
        "robot_id_input": ROBOT_ID,
        "image_b64_input": b64_img
    }

    try:
        async with MCP_CLIENT:
            response = await MCP_CLIENT.call_tool(TOOL_NAME, payload)
            if response.is_error:
                error_text = response.content.text if response.content else "Unknown error"
                raise Exception(f"MCP Tool Error: {error_text}")

            raw_text = response.content.text
            response_dict = ast.literal_eval(raw_text)
            vlm_result = response_dict.get("result", {})

            # Extract all fields
            description_out = vlm_result.get("description", "")
            human_out = vlm_result.get("human", "")
            environment_out = vlm_result.get("environment", "")
            indoor_outdoor_out = vlm_result.get("indoor_or_outdoor", "")
            lighting_condition_out = vlm_result.get("lighting_condition", "")
            animals_list = vlm_result.get("animals", [])
            hazards_list = vlm_result.get("hazards", [])
            objects_list = vlm_result.get("objects", [])

            # Convert lists to strings
            objects_str = ", ".join(objects_list) if isinstance(objects_list, list) else str(objects_list)
            animals_str = ", ".join(animals_list) if isinstance(animals_list, list) else str(animals_list)
            hazards_str = ", ".join(hazards_list) if isinstance(hazards_list, list) else str(hazards_list)

            return (
                description_out,
                environment_out,
                indoor_outdoor_out,
                lighting_condition_out,
                human_out,
                animals_str,
                objects_str,
                hazards_str
            )

    except Exception as e:
        print(f"Error calling MCP API: {e}")
        import traceback
        traceback.print_exc()
        return f"Error: {e}", "", "", "", "", "", "", ""


# -------------------------------
# Gradio UI
# -------------------------------
with gr.Blocks() as demo:
    # Hugging Face OAuth login button
    gr.LoginButton()
    gr.Markdown("## 🎥 Robot Vision Webcam Stream (MCP Client)")
    with gr.Row():
        webcam_input = gr.Image(label="Captured from Web-Cam", sources=["upload", "webcam"], type="pil")
        with gr.Column():
            description_out = gr.Textbox(label="Description", lines=5)
            environment_out = gr.Textbox(label="Environment", lines=3)
            indoor_outdoor_out = gr.Textbox(label="Indoor/Outdoor", lines=1)
            lighting_condition_out = gr.Textbox(label="Lighting Condition", lines=1)
            human_out = gr.Textbox(label="Human Detected", lines=3)
            animals_out = gr.Textbox(label="Animals Detected", lines=2)
            objects_out = gr.Textbox(label="Objects Detected", lines=2)
            hazards_out = gr.Textbox(label="Hazards Identified", lines=2)

    # Only webcam input in inputs; Gradio automatically injects oauth_token
    webcam_input.stream(
        process_webcam_stream_async,
        inputs=[webcam_input],
        outputs=[
            description_out,
            environment_out,
            indoor_outdoor_out,
            lighting_condition_out,
            human_out,
            animals_out,
            objects_out,
            hazards_out
        ],
        stream_every=1.0
    )

if __name__ == "__main__":
    demo.launch()