Update app.py

app.py

@@ -1,7 +1,231 @@
 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, snapshot_download
+from safetensors.torch import load_file
+import torch
+import subprocess
 
-def greet(name):
-    return "Hello " + name + "!!"
+# --- Conversion Function: Safetensors (UNet) → GGUF ---
+def convert_unet_to_gguf(safetensors_path, output_dir, progress=gr.Progress()):
+    """
+    Converts a UNet safetensors file to GGUF using gguf-connector's CLI (t2 or t).
+    Assumes the file is named 'unet.safetensors'.
+    """
+    progress(0.1, desc="Starting UNet to GGUF conversion...")
+
+    try:
+        # Ensure gguf-connector is available
+        import gguf_connector  # noqa
+
+        # Copy input to working dir because ggc expects files in current dir
+        work_dir = tempfile.mkdtemp()
+        input_path = os.path.join(work_dir, "unet.safetensors")
+        shutil.copy(safetensors_path, input_path)
+
+        # GGUF output will be named automatically like unet.safetensors -> unet.gguf
+        gguf_output_path = os.path.join(work_dir, "unet.gguf")
+
+        progress(0.3, desc="Running gguf-connector (t2: safetensors → GGUF)...")
+
+        # Use 'ggc t2' for conversion (beta: unlimited)
+        # This is interactive, so we must simulate input via echo or expect
+        # But since ggc t2 may be interactive, we try non-interactive fallback:
+        # Unfortunately, ggc does not support non-interactive mode robustly.
+        # So we simulate by running in dir and hoping it picks the only file.
+
+        # Change working dir so ggc sees the file
+        original_cwd = os.getcwd()
+        os.chdir(work_dir)
+
+        try:
+            # Launch ggc t2 and auto-select first file via input redirection
+            # This is fragile but best-effort
+            result = subprocess.run(
+                ["ggc", "t2"],
+                input="1\n",  # select first model
+                text=True,
+                capture_output=True,
+                timeout=300
+            )
+            if result.returncode != 0:
+                raise RuntimeError(f"ggc t2 failed: {result.stderr}")
+        finally:
+            os.chdir(original_cwd)
+
+        if not os.path.exists(gguf_output_path):
+            # Try alternative naming
+            candidates = [f for f in os.listdir(work_dir) if f.endswith(".gguf")]
+            if not candidates:
+                raise FileNotFoundError("No GGUF file generated by ggc t2")
+            gguf_output_path = os.path.join(work_dir, candidates[0])
+
+        # Move to output dir
+        final_gguf_path = os.path.join(output_dir, "unet.gguf")
+        shutil.move(gguf_output_path, final_gguf_path)
+
+        # Also save minimal config
+        config_path = os.path.join(output_dir, "config.json")
+        with open(config_path, "w") as f:
+            json.dump({
+                "model_type": "unet",
+                "format": "gguf",
+                "source": "converted from safetensors"
+            }, f)
+
+        progress(1.0, desc="Conversion to GGUF complete!")
+        return True, "UNet converted to GGUF successfully."
+
+    except Exception as e:
+        return False, str(e)
+    finally:
+        if 'work_dir' in locals():
+            shutil.rmtree(work_dir, ignore_errors=True)
+
+# --- Main Processing Function ---
+def process_and_upload_unet_to_gguf(repo_url, hf_token, new_repo_id, private_repo, progress=gr.Progress()):
+    if not all([repo_url, hf_token, new_repo_id]):
+        return None, "❌ Error: Please fill in all fields.", ""
+
+    if not re.match(r"^[a-zA-Z0-9._-]+/[a-zA-Z0-9._-]+$", new_repo_id):
+        return None, "❌ Error: Invalid repository ID format. Use 'username/model-name'.", ""
+
+    temp_dir = tempfile.mkdtemp()
+    output_dir = tempfile.mkdtemp()
+
+    try:
+        # Authenticate
+        progress(0.05, desc="Logging into Hugging Face...")
+        api = HfApi(token=hf_token)
+        user_info = api.whoami()
+        user_name = user_info['name']
+        progress(0.1, desc=f"Logged in as {user_name}.")
+
+        # Parse source repo
+        clean_url = repo_url.strip().rstrip("/")
+        if "huggingface.co" not in clean_url:
+            return None, "❌ Source must be a Hugging Face model repo.", ""
+        src_repo_id = clean_url.replace("https://huggingface.co/", "")
+
+        # Download only unet.safetensors
+        progress(0.15, desc="Downloading unet.safetensors...")
+        safetensors_path = hf_hub_download(
+            repo_id=src_repo_id,
+            filename="unet.safetensors",
+            cache_dir=temp_dir,
+            token=hf_token
+        )
+        progress(0.3, desc="Download complete.")
+
+        # Convert
+        success, msg = convert_unet_to_gguf(safetensors_path, output_dir, progress)
+        if not success:
+            return None, f"❌ Conversion failed: {msg}", ""
+
+        # Create new repo
+        progress(0.8, desc="Creating new repository...")
+        api.create_repo(
+            repo_id=new_repo_id,
+            private=private_repo,
+            repo_type="model",
+            exist_ok=True
+        )
+
+        # Generate README
+        readme = f"""---
+library_name: diffusers
+tags:
+- gguf
+- unet
+- diffusion
+- converted-by-gradio
+---
+
+# GGUF UNet Model
+
+Converted from: [`{src_repo_id}`](https://huggingface.co/{src_repo_id})  
+File: `unet.safetensors` → `unet.gguf`
+
+Converted by: {user_name}  
+Date: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+
+> ⚠️ This is a GGUF-quantized UNet for storage efficiency. Use with compatible GGUF-aware inference engines.
+"""
+        with open(os.path.join(output_dir, "README.md"), "w") as f:
+            f.write(readme)
+
+        # Upload
+        progress(0.9, desc="Uploading to Hugging Face Hub...")
+        api.upload_folder(
+            repo_id=new_repo_id,
+            folder_path=output_dir,
+            repo_type="model",
+            token=hf_token,
+            commit_message="Upload UNet GGUF conversion"
+        )
+
+        progress(1.0, desc="✅ Done!")
+        result_html = f"""
+✅ Success!  
+Your GGUF UNet is uploaded to: [{new_repo_id}](https://huggingface.co/{new_repo_id})  
+Visibility: {'Private' if private_repo else 'Public'}
+"""
+        return gr.HTML(result_html), "✅ Conversion and upload successful!", ""
+
+    except Exception as e:
+        return None, f"❌ Error: {str(e)}", ""
+    finally:
+        shutil.rmtree(temp_dir, ignore_errors=True)
+        shutil.rmtree(output_dir, ignore_errors=True)
+
+# --- Gradio UI ---
+with gr.Blocks(title="UNet Safetensors → GGUF Converter") as demo:
+    gr.Markdown("# 🔄 UNet (Safetensors) to GGUF Converter")
+    gr.Markdown("Converts `unet.safetensors` from a Hugging Face model repo to GGUF format for compact storage.")
+
+    with gr.Row():
+        with gr.Column():
+            repo_url = gr.Textbox(
+                label="Source Model Repository URL",
+                placeholder="https://huggingface.co/Yabo/FramePainter",
+                info="Must contain 'unet.safetensors'"
+            )
+            hf_token = gr.Textbox(
+                label="Hugging Face Token",
+                type="password",
+                info="Write-access token from https://huggingface.co/settings/tokens"
+            )
+        with gr.Column():
+            new_repo_id = gr.Textbox(
+                label="New Repository ID",
+                placeholder="your-username/framepainter-unet-gguf",
+                info="Format: username/model-name"
+            )
+            private_repo = gr.Checkbox(label="Make Private", 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_unet_to_gguf,
+        inputs=[repo_url, hf_token, new_repo_id, private_repo],
+        outputs=[repo_link_output, status_output],
+        show_progress=True
+    )
+
+    gr.Examples(
+        examples=[
+            ["https://huggingface.co/Yabo/FramePainter"]
+        ],
+        inputs=[repo_url]
+    )
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()