app.py

import gradio as gr
import os
import tempfile
import shutil
import re
import json
import datetime
from pathlib import Path
from huggingface_hub import HfApi, hf_hub_download
from safetensors.torch import load_file, save_file
import torch

# Optional ModelScope integration
try:
    from modelscope.hub.snapshot_download import snapshot_download as ms_snapshot_download
    from modelscope.hub.file_download import model_file_download as ms_file_download
    from modelscope.hub.api import HubApi as ModelScopeApi
    MODELScope_AVAILABLE = True
except ImportError:
    MODELScope_AVAILABLE = False

# --- Conversion Function: Safetensors → FP8 Safetensors ---
def convert_safetensors_to_fp8(safetensors_path, output_dir, fp8_format, progress=gr.Progress()):
    progress(0.1, desc="Starting FP8 conversion...")

    try:
        def read_safetensors_metadata(path):
            with open(path, 'rb') as f:
                header_size = int.from_bytes(f.read(8), 'little')
                header_json = f.read(header_size).decode('utf-8')
                header = json.loads(header_json)
                return header.get('__metadata__', {})

        metadata = read_safetensors_metadata(safetensors_path)
        progress(0.3, desc="Loaded model metadata.")

        state_dict = load_file(safetensors_path)
        progress(0.5, desc="Loaded model weights.")

        if fp8_format == "e5m2":
            fp8_dtype = torch.float8_e5m2
        else:
            fp8_dtype = torch.float8_e4m3fn

        sd_pruned = {}
        total = len(state_dict)
        for i, key in enumerate(state_dict):
            progress(0.5 + 0.4 * (i / total), desc=f"Converting tensor {i+1}/{total} to FP8 ({fp8_format})...")
            if state_dict[key].dtype in [torch.float16, torch.float32, torch.bfloat16]:
                sd_pruned[key] = state_dict[key].to(fp8_dtype)
            else:
                sd_pruned[key] = state_dict[key]

        base_name = os.path.splitext(os.path.basename(safetensors_path))[0]
        output_path = os.path.join(output_dir, f"{base_name}-fp8-{fp8_format}.safetensors")
        save_file(sd_pruned, output_path, metadata={"format": "pt", "fp8_format": fp8_format, **metadata})
        progress(0.9, desc="Saved FP8 safetensors file.")

        progress(1.0, desc="FP8 conversion complete!")
        return True, f"Model successfully pruned to FP8 ({fp8_format})."

    except Exception as e:
        return False, str(e)

# --- Source download helper ---
def download_safetensors_file(
    source_type,
    repo_url,
    filename,
    hf_token=None,
    modelscope_token=None,
    progress=gr.Progress()
):
    temp_dir = tempfile.mkdtemp()
    try:
        if source_type == "huggingface":
            clean_url = repo_url.strip().rstrip("/")
            if "huggingface.co" not in clean_url:
                raise ValueError("Invalid Hugging Face URL")
            src_repo_id = clean_url.replace("https://huggingface.co/", "")
            safetensors_path = hf_hub_download(
                repo_id=src_repo_id,
                filename=filename,
                cache_dir=temp_dir,
                token=hf_token
            )
        elif source_type == "modelscope":
            if not MODELScope_AVAILABLE:
                raise ImportError("ModelScope not installed. Install with: pip install modelscope")
            clean_url = repo_url.strip().rstrip("/")
            if "modelscope.cn" in clean_url:
                src_repo_id = "/".join(clean_url.split("/")[-2:])
            else:
                src_repo_id = repo_url.strip()
            if modelscope_token:
                os.environ["MODELSCOPE_CACHE"] = temp_dir
                safetensors_path = ms_file_download(
                    model_id=src_repo_id,
                    file_path=filename,
                    token=modelscope_token
                )
            else:
                safetensors_path = ms_file_download(
                    model_id=src_repo_id,
                    file_path=filename
                )
        else:
            raise ValueError("Unknown source type")

        return safetensors_path, temp_dir
    except Exception as e:
        shutil.rmtree(temp_dir, ignore_errors=True)
        raise e

# --- Upload helper ---
def upload_to_target(
    target_type,
    new_repo_id,
    output_dir,
    fp8_format,
    hf_token=None,
    modelscope_token=None,
    private_repo=False,
    progress=gr.Progress()
):
    if target_type == "huggingface":
        if not hf_token:
            raise ValueError("Hugging Face token required")
        api = HfApi(token=hf_token)
        api.create_repo(
            repo_id=new_repo_id,
            private=private_repo,
            repo_type="model",
            exist_ok=True
        )
        api.upload_folder(
            repo_id=new_repo_id,
            folder_path=output_dir,
            repo_type="model",
            token=hf_token,
            commit_message=f"Upload FP8 ({fp8_format}) model"
        )
        return f"https://huggingface.co/{new_repo_id}"

    elif target_type == "modelscope":
        if not MODELScope_AVAILABLE:
            raise ImportError("ModelScope not installed")
        api = ModelScopeApi()
        if modelscope_token:
            api.login(modelscope_token)
        # ModelScope requires model_type and license
        api.push_model(
            model_id=new_repo_id,
            model_dir=output_dir,
            commit_message=f"Upload FP8 ({fp8_format}) model"
        )
        return f"https://modelscope.cn/models/{new_repo_id}"

    else:
        raise ValueError("Unknown target type")

# --- Main Processing Function ---
def process_and_upload_fp8(
    source_type,
    repo_url,
    safetensors_filename,
    fp8_format,
    target_type,
    new_repo_id,
    hf_token,
    modelscope_token,
    private_repo,
    progress=gr.Progress()
):
    required_fields = [repo_url, safetensors_filename, new_repo_id]
    if source_type == "huggingface":
        required_fields.append(hf_token)
    if target_type == "huggingface":
        required_fields.append(hf_token)
    if target_type == "modelscope" and modelscope_token:
        required_fields.append(modelscope_token)

    if not all(required_fields):
        return None, "❌ Error: Please fill in all required fields.", ""

    if not re.match(r"^[a-zA-Z0-9._-]+/[a-zA-Z0-9._-]+$", new_repo_id):
        return None, "❌ Invalid repository ID format. Use 'username/model-name'.", ""

    temp_dir = None
    output_dir = tempfile.mkdtemp()

    try:
        # Authenticate & download
        progress(0.05, desc="Authenticating and downloading...")
        safetensors_path, temp_dir = download_safetensors_file(
            source_type=source_type,
            repo_url=repo_url,
            filename=safetensors_filename,
            hf_token=hf_token,
            modelscope_token=modelscope_token,
            progress=progress
        )
        progress(0.25, desc="Download complete.")

        # Convert
        success, msg = convert_safetensors_to_fp8(safetensors_path, output_dir, fp8_format, progress)
        if not success:
            return None, f"❌ Conversion failed: {msg}", ""

        # Upload
        progress(0.92, desc="Uploading model...")
        repo_url_final = upload_to_target(
            target_type=target_type,
            new_repo_id=new_repo_id,
            output_dir=output_dir,
            fp8_format=fp8_format,
            hf_token=hf_token,
            modelscope_token=modelscope_token,
            private_repo=private_repo,
            progress=progress
        )

        # README
        base_name = os.path.splitext(safetensors_filename)[0]
        fp8_filename = f"{base_name}-fp8-{fp8_format}.safetensors"
        readme = f"""---
library_name: diffusers
tags:
- fp8
- safetensors
- pruned
- diffusion
- converted-by-gradio
- fp8-{fp8_format}
---

# FP8 Pruned Model ({fp8_format.upper()})

Converted from: `{repo_url}`  
File: `{safetensors_filename}` → `{fp8_filename}`

Quantization: **FP8 ({fp8_format.upper()})**  
Converted on: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}

> ⚠️ Requires PyTorch ≥ 2.1 and compatible hardware for FP8 acceleration.
"""
        readme_path = os.path.join(output_dir, "README.md")
        with open(readme_path, "w") as f:
            f.write(readme)

        # Re-upload README if needed (for ModelScope, already included; for HF, upload separately)
        if target_type == "huggingface":
            HfApi(token=hf_token).upload_file(
                path_or_fileobj=readme_path,
                path_in_repo="README.md",
                repo_id=new_repo_id,
                repo_type="model",
                token=hf_token
            )

        progress(1.0, desc="✅ Done!")
        result_html = f"""
✅ Success!  
Your FP8 model is uploaded to: <a href="{repo_url_final}" target="_blank">{new_repo_id}</a>  
Source: {source_type.title()} → Target: {target_type.title()}
"""
        return gr.HTML(result_html), "✅ FP8 conversion and upload successful!", ""

    except Exception as e:
        return None, f"❌ Error: {str(e)}", ""
    finally:
        if temp_dir:
            shutil.rmtree(temp_dir, ignore_errors=True)
        shutil.rmtree(output_dir, ignore_errors=True)

# --- Gradio UI ---
with gr.Blocks(title="Safetensors → FP8 Pruner (HF + ModelScope)") as demo:
    gr.Markdown("# 🔄 Safetensors to FP8 Pruner")
    gr.Markdown("Convert `.safetensors` models to **FP8** and upload to **Hugging Face** or **ModelScope**.")

    with gr.Row():
        with gr.Column():
            source_type = gr.Radio(
                choices=["huggingface", "modelscope"],
                value="huggingface",
                label="Source Platform"
            )
            repo_url = gr.Textbox(
                label="Source Repository URL",
                placeholder="e.g., https://huggingface.co/Yabo/FramePainter OR your-modelscope-id",
                info="Hugging Face URL or ModelScope model ID"
            )
            safetensors_filename = gr.Textbox(
                label="Safetensors Filename",
                placeholder="unet_diffusion_pytorch_model.safetensors"
            )
            fp8_format = gr.Radio(
                choices=["e4m3fn", "e5m2"],
                value="e5m2",
                label="FP8 Format",
                info="E5M2: wider range; E4M3FN: better near-zero precision"
            )
            hf_token = gr.Textbox(
                label="Hugging Face Token (if using HF)",
                type="password"
            )
            modelscope_token = gr.Textbox(
                label="ModelScope Token (optional)",
                type="password",
                visible=MODELScope_AVAILABLE
            )
        with gr.Column():
            target_type = gr.Radio(
                choices=["huggingface", "modelscope"],
                value="huggingface",
                label="Target Platform"
            )
            new_repo_id = gr.Textbox(
                label="New Repository ID",
                placeholder="your-username/my-model-fp8"
            )
            private_repo = gr.Checkbox(label="Make Private (HF only)", value=False)

    convert_btn = gr.Button("🚀 Convert & Upload", variant="primary")

    with gr.Row():
        status_output = gr.Markdown()
        repo_link_output = gr.HTML()

    convert_btn.click(
        fn=process_and_upload_fp8,
        inputs=[
            source_type,
            repo_url,
            safetensors_filename,
            fp8_format,
            target_type,
            new_repo_id,
            hf_token,
            modelscope_token,
            private_repo
        ],
        outputs=[repo_link_output, status_output],
        show_progress=True
    )

    gr.Examples(
        examples=[
            ["huggingface", "https://huggingface.co/Yabo/FramePainter", "unet_diffusion_pytorch_model.safetensors", "e5m2", "huggingface"]
        ],
        inputs=[source_type, repo_url, safetensors_filename, fp8_format, target_type]
    )

demo.launch()