app.py

import gradio as gr
import json
import base64
import os
from huggingface_hub import HfApi
import gradio as gr

# =====================================================
# Settings
# =====================================================
REPO_ID = "OppaAi/Robot-MCP"   # 你的 HuggingFace Space repo
REPO_TYPE = "space"
UPLOAD_FILENAME = "tmp.jpg"

api = HfApi()

# =====================================================
# Function: Save & Upload Base64 Image
# =====================================================
def save_and_upload_base64_image(base64_str):
    try:
        # Remove base64 header (if exists)
        if "," in base64_str:
            base64_str = base64_str.split(",", 1)[1]

        # Decode and save locally
        with open(UPLOAD_FILENAME, "wb") as f:
            f.write(base64.b64decode(base64_str))

        print("Saved tmp.jpg locally.")

        # Upload to repo so you can see it in Files
        api.upload_file(
            path_or_fileobj=UPLOAD_FILENAME,
            path_in_repo=UPLOAD_FILENAME,   # Upload to root folder
            repo_id=REPO_ID,
            repo_type=REPO_TYPE,
        )

        print("Uploaded tmp.jpg to HuggingFace Repo.")
        return "Success: tmp.jpg uploaded to HF repo!"

    except Exception as e:
        print("Error:", str(e))
        return f"Error: {str(e)}"


# =====================================================
# Gradio UI
# =====================================================
def handle_input(base64_image):
    print("Received base64 image.")
    return save_and_upload_base64_image(base64_image)


with gr.Blocks() as demo:
    gr.Markdown("## 🚀 Base64 → tmp.jpg → Upload to HF Repo")
    
    base64_input = gr.Textbox(label="Input Base64 Image String")
    output_msg = gr.Textbox(label="Result")

    run_btn = gr.Button("Upload")
    run_btn.click(handle_input, inputs=base64_input, outputs=output_msg)


# Run Gradio
if __name__ == "__main__":
    demo.launch()