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

# -------------------------------
# MCP server info
# -------------------------------
ROBOT_ID = "CV_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"

MCP_SERVER_URL = "https://mcp-1st-birthday-cv-mcp-server.hf.space/gradio_api/mcp/"
SERVER_NAME = "CV_MCP_Server"
TOOL_NAME = "CV_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):
    if image is None:
        return "", "", "", "", "", "", "", ""

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

    # Convert image to Base64
    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:
                # Handle error content safely
                error_msg = "Unknown Error"
                if hasattr(response, 'content') and isinstance(response.content, list):
                    error_msg = " ".join([getattr(item, 'text', '') for item in response.content])
                raise Exception(f"MCP Tool Error: {error_msg}")

            # ---------------------------------------------------------
            # FIX: Handle List Content
            # The 'content' is a list of objects (e.g., TextContent).
            # We iterate through the list and join the text parts.
            # ---------------------------------------------------------
            raw_text = ""
            if hasattr(response, 'content') and isinstance(response.content, list):
                for item in response.content:
                    # Check if the item has a 'text' attribute
                    if hasattr(item, 'text'):
                        raw_text += item.text
            else:
                # Fallback for unexpected structure
                raw_text = str(response)

            # 6. PARSE RESPONSE
            try:
                response_dict = json.loads(raw_text)
            except json.JSONDecodeError:
                try:
                    response_dict = ast.literal_eval(raw_text)
                except Exception:
                    # If parsing fails completely, return the raw text in description
                    return f"Parsing Error. Raw output: {raw_text}", "", "", "", "", "", "", ""
            
            vlm_result = response_dict.get("result", {})

            # 7. EXTRACT DATA
            description_out = vlm_result.get("description", "")
            environment_out = vlm_result.get("environment", "")
            indoor_outdoor_out = vlm_result.get("indoor_or_outdoor", "")
            lighting_condition_out = vlm_result.get("lighting_condition", "")
            human_out = vlm_result.get("human", "")
            animals_out = vlm_result.get("animals", "")
            objects_list = vlm_result.get("objects", [])
            hazards_out = vlm_result.get("hazards", "")

            objects_str = ", ".join(objects_list) if isinstance(objects_list, list) else str(objects_list)

            return (
                description_out,
                environment_out,
                indoor_outdoor_out,
                lighting_condition_out,
                human_out,
                animals_out,
                objects_str,
                hazards_out
            )

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


# -------------------------------
# Gradio UI
# -------------------------------
with gr.Blocks() as demo:
    gr.Markdown("## 🎥 Robot Vision Webcam Stream (MCP Client)")
    gr.Markdown(
        """
This interface captures a live webcam feed and sends each frame to the MCP Client for analysis.
The system extracts detailed information from the scene — including descriptions, detected objects,
humans, animals, environmental context, lighting conditions, and potential hazards.
Use this dashboard to observe how the robot interprets the world in real time.
        """
    )

    with gr.Row():
        webcam_input = gr.Image(
            label="Captured from Web-Cam", 
            sources=["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)

    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(ssr_mode=False)