import os
import base64
import time
import io
import gradio as gr
from fastmcp import Client
from fastmcp.client import StreamableHttpTransport 
import asyncio
from dotenv import load_dotenv
import ast # Import the Abstract Syntax Tree module

# Load environment variables (ensure .env is set up locally)
load_dotenv()

ROBOT_ID = "Robot_MCP_Client"
HF_TOKEN = os.environ.get("HF_TOKEN")
if not HF_TOKEN:
    print("Warning: HF_TOKEN not found. API calls may fail.")
    HF_TOKEN = "missing_token_placeholder" 

# The MCP URL of your remote server
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 the MCP client globally
HTTP_TRANSPORT = StreamableHttpTransport(url=MCP_SERVER_URL)
MCP_CLIENT = Client(transport=HTTP_TRANSPORT, name=SERVER_NAME)


async def process_webcam_stream_async(image):
    """Send webcam image to HF MCP Server using MCP protocol and get result"""
    if image is None:
        return "", "", "", ""

    if HF_TOKEN == "missing_token_placeholder":
        return "Error: HF_TOKEN not set locally.", "", "", ""

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

    payload = {
        "hf_token_input": HF_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[0].text if response.content and isinstance(response.content, list) else "Unknown error"
                raise Exception(f"MCP Tool Error: {error_text}")

            # Use ast.literal_eval because your server returns Python-formatted strings (single quotes)
            raw_text = response.content[0].text
            response_dict = ast.literal_eval(raw_text)
            
            # --- 👇 EXTRACTING EACH FIELD CORRECTLY 👇 ---
            vlm_result = response_dict.get("result", {})
            
            description_out = vlm_result.get("description", "")
            human_out = vlm_result.get("human", "")
            objects_list = vlm_result.get("objects", [])
            environment_out = vlm_result.get("environment", "")

            # Format the objects list into a single string for display
            objects_str = ", ".join(objects_list) if isinstance(objects_list, list) else str(objects_list)

            return (
                description_out,
                human_out,
                objects_str,
                environment_out
            )
            
    except Exception as e:
        print(f"Error calling remote MCP API: {e}") 
        import traceback
        traceback.print_exc() # Helps debug other errors
        return f"Error: {e}", "", "", ""


with gr.Blocks() as demo:
    gr.Markdown("## 🎥 Robot Vision Webcam Stream (using MCP Client)")
    gr.Markdown("""   
    ### 🔑 Hugging Face Token Required
    To use this application, you must set a valid **Hugging Face API Token** in your local environment variables: `HF_TOKEN`.
    **A write token is required** to upload images to the public dataset associated with this space. The resource usage for VLM inference will be tracked against *your* account.
    """)
    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")
            human_out = gr.Textbox(label="Human")
            objects_out = gr.Textbox(label="Objects")
            environment_out = gr.Textbox(label="Environment")

    webcam_input.stream(
        process_webcam_stream_async,
        inputs=[webcam_input],
        outputs=[description_out, human_out, objects_out, environment_out],
        stream_every=0.5
    )

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