app.py

# app.py
import os
import base64
import json
import gradio as gr
from huggingface_hub import upload_file, InferenceClient
from datetime import datetime
import traceback
import threading
from typing import Optional, Dict, Any, Tuple

from fastmcp import FastMCP


HF_DATASET_REPO = "OppaAI/Robot_MCP"
HF_VLM_MODEL = "Qwen/Qwen2.5-VL-7B-Instruct"

mcp = FastMCP("Robot_MCP")

# -----------------------------------------------------
# Register Robot Tools (MCP)
# -----------------------------------------------------
@mcp.tool()
def speak(text: str, emotion: str = "neutral"):
    """Robot speech output"""
    return {
        "status": "success",
        "action_executed": "speak",
        "payload": {"text": text, "emotion": emotion},
    }


@mcp.tool()
def navigate(direction: str, distance_meters: float):
    """Move robot safely"""
    if distance_meters > 5.0:
        return {"status": "error", "message": "Safety limit exceeded"}
    return {
        "status": "success",
        "action_executed": "navigate",
        "payload": {"direction": direction, "distance": distance_meters},
    }


@mcp.tool()
def scan_hazard(hazard_type: str, severity: str):
    """Hazard scan + log"""
    timestamp = datetime.now().isoformat()
    return {
        "status": "warning_logged",
        "log": f"[{timestamp}] HAZARD: {hazard_type} (Severity: {severity})",
    }


@mcp.tool()
def analyze_human(clothing_color: str, estimated_action: str):
    """Human detection description"""
    return {
        "status": "human_tracked",
        "details": f"Human wearing {clothing_color} is {estimated_action}",
    }


# -----------------------------------------------------
# Save and Upload Image
# -----------------------------------------------------
def save_and_upload_image(image_b64: str, hf_token: str):
    try:
        image_bytes = base64.b64decode(image_b64)
        size_bytes = len(image_bytes)
        print("[debug] decoded image bytes:", size_bytes)

        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
        local_path = f"/tmp/robot_img_{timestamp}.jpg"

        with open(local_path, "wb") as f:
            f.write(image_bytes)

        print("[debug] wrote local tmp file:", local_path)

        filename = f"robot_{timestamp}.jpg"

        upload_file(
            path_or_fileobj=local_path,
            path_in_repo=filename,
            repo_id=HF_DATASET_REPO,
            token=hf_token,
            repo_type="dataset",
        )

        print("[debug] upload successful:", filename)

        url = f"https://huggingface.co/datasets/{HF_DATASET_REPO}/resolve/main/{filename}"
        return local_path, url, filename, size_bytes

    except Exception:
        traceback.print_exc()
        return None, None, None, 0


# -----------------------------------------------------
# JSON Parsing Helper
# -----------------------------------------------------
def safe_parse_json_from_text(text: str):
    if not text:
        return None
    try:
        return json.loads(text)
    except:
        pass

    cleaned = text.strip().strip("`")
    try:
        start = cleaned.find("{")
        end = cleaned.rfind("}")
        if start >= 0 and end > start:
            return json.loads(cleaned[start : end + 1])
    except:
        pass

    return None


# -----------------------------------------------------
# Only allow tools from MCP registry
# -----------------------------------------------------
def validate_and_call_tool(tool_name: str, tool_args: dict):
    if tool_name not in mcp.tools:
        return {"error": f"Unknown or unauthorized tool '{tool_name}'"}
    try:
        return mcp.tools[tool_name](**tool_args)
    except Exception as e:
        traceback.print_exc()
        return {"error": f"Tool error: {str(e)}"}


# -----------------------------------------------------
# Main Pipeline
# -----------------------------------------------------
def process_and_describe(payload):

    if isinstance(payload, str):
        try:
            payload = json.loads(payload)
        except:
            return {"error": "Invalid JSON payload"}

    print("\n========== NEW REQUEST ==========")
    print("[debug] Incoming payload:", payload)

    hf_token = payload.get("hf_token")
    if not hf_token:
        return {"error": "hf_token missing"}

    robot_id = payload.get("robot_id", "unknown")
    image_b64 = payload.get("image_b64")
    if not image_b64:
        return {"error": "image_b64 missing"}

    # Save + Upload
    local_tmp_path, hf_url, filename, size_bytes = save_and_upload_image(
        image_b64, hf_token
    )

    if not hf_url:
        return {"error": "Image upload failed"}

    print("[debug] HF image URL:", hf_url)

    # VLM SYSTEM PROMPT
    system_prompt = """
Respond in STRICT JSON ONLY. Format:
{
 "description": "short visual description",
 "tool_name": "one of: speak, navigate, scan_hazard, analyze_human",
 "arguments": { ... }
}
"""

    messages = [
        {"role": "system", "content": system_prompt},
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Analyze the image and choose ONE tool."},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"},
                },
            ],
        },
    ]

    # VLM CALL
    print("[debug] Calling VLM model...")
    client = InferenceClient(token=hf_token)

    response = client.chat.completions.create(
        model=HF_VLM_MODEL,
        messages=messages,
        max_tokens=300,
        temperature=0.1,
    )

    vlm_output = response.choices[0].message.content.strip()

    print("\n------ VLM RAW OUTPUT ------")
    print(vlm_output)
    print("------ END VLM RAW ------\n")

    parsed = safe_parse_json_from_text(vlm_output)

    if parsed is None:
        return {
            "status": "model_no_json",
            "robot_id": robot_id,
            "image_url": hf_url,
            "vlm_raw": vlm_output,
            "message": "VLM returned invalid JSON",
        }

    tool_name = parsed.get("tool_name")
    tool_args = parsed.get("arguments") or {}

    tool_result = validate_and_call_tool(tool_name, tool_args)

    return {
        "status": "success",
        "robot_id": robot_id,
        "image_url": hf_url,
        "file_size_bytes": size_bytes,
        "vlm_description": parsed.get("description"),
        "chosen_tool": tool_name,
        "tool_arguments": tool_args,
        "tool_execution_result": tool_result,
        "vlm_raw": vlm_output,
    }


# ------------------------------
# Gradio Interface
# ------------------------------
iface = gr.Interface(
    fn=process_and_describe,
    inputs=gr.JSON(label="Input JSON"),
    outputs=gr.JSON(label="Output JSON"),
    api_name="predict",
    flagging_mode="never"
)

# ------------------------------
# Main Entry
# ------------------------------
if __name__ == "__main__":
    print("[Gradio] Launching interface...")
    iface.launch(server_name="0.0.0.0", server_port=7860)