Delete app.py

app.py

@@ -1,182 +0,0 @@
-import torch
-import spaces
-
-from transformers import Qwen3_5ForConditionalGeneration, AutoProcessor
-from huggingface_hub import create_repo, upload_large_folder, login
-import os
-import gradio as gr
-
-@spaces.GPU(duration=900)
-def load_and_reupload_model(model_name, new_repo_id, hf_token, max_shard_size="4.4GB"):
-
-    log_output = []
-
-    try:
-
-        # -------------------------
-        # Validate inputs
-        # -------------------------
-        if not model_name or not new_repo_id or not hf_token:
-            return "❌ Model name, repo id, and token are required."
-
-        # -------------------------
-        # Login to HuggingFace
-        # -------------------------
-        login(token=hf_token)
-        log_output.append("✅ Hugging Face login successful")
-
-        # -------------------------
-        # Create repository
-        # -------------------------
-        create_repo(
-            repo_id=new_repo_id,
-            private=True,
-            exist_ok=True
-        )
-
-        log_output.append(f"✅ Repo ready: {new_repo_id}")
-
-        # -------------------------
-        # Load processor
-        # -------------------------
-        log_output.append(f"🔄 Loading processor: {model_name}")
-
-        processor = AutoProcessor.from_pretrained(
-            model_name,
-            trust_remote_code=True
-        )
-
-        log_output.append("✅ Processor loaded")
-
-        # -------------------------
-        # Load model
-        # -------------------------
-        log_output.append(f"🔄 Loading model: {model_name}")
-
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        model = Qwen3_5ForConditionalGeneration.from_pretrained(
-            model_name,
-            dtype="auto",
-            device_map="auto",
-            trust_remote_code=True,
-            use_safetensors=True
-        )
-
-        model.eval()
-
-        log_output.append(f"✅ Model loaded on {device}")
-
-        # -------------------------
-        # Save locally
-        # -------------------------
-        local_dir = new_repo_id.split("/")[-1]
-
-        os.makedirs(local_dir, exist_ok=True)
-
-        log_output.append(
-            f"🔄 Saving model shards (max_shard_size={max_shard_size})"
-        )
-
-        model.save_pretrained(
-            local_dir,
-            max_shard_size=max_shard_size
-        )
-
-        processor.save_pretrained(local_dir)
-
-        log_output.append("✅ Model + processor saved locally")
-
-        # -------------------------
-        # Upload using upload_large_folder
-        # -------------------------
-        log_output.append("🔄 Uploading model to HuggingFace...")
-
-        upload_large_folder(
-            repo_id=new_repo_id,
-            repo_type="model",
-            folder_path=local_dir,
-            revision="main"
-        )
-
-        log_output.append("🚀 Upload completed successfully!")
-
-    except Exception as e:
-
-        log_output.append(f"❌ Error: {str(e)}")
-
-    return "\n".join(log_output)
-
-
-# =====================================================
-# Gradio UI
-# =====================================================
-
-with gr.Blocks(theme="soft") as demo:
-
-    gr.Markdown(
-        """
-# 🚀 Qwen3-VL Model Sharder & Re-Uploader
-
-This tool will:
-
-1️⃣ Download a **Qwen3-VL model**  
-2️⃣ Save it locally with **smaller shards**  
-3️⃣ Upload it to a **private Hugging Face repository**
-
-Uses **upload_large_folder()** for reliable large uploads.
-"""
-    )
-
-    with gr.Row():
-
-        with gr.Column(scale=2):
-
-            model_name = gr.Textbox(
-                label="Original Model Name",
-                value="Qwen/Qwen3-VL-2B-Instruct"
-            )
-
-            new_repo_id = gr.Textbox(
-                label="New Repository ID",
-                placeholder="username/my-private-qwen3vl"
-            )
-
-            hf_token = gr.Textbox(
-                label="HuggingFace Write Token",
-                type="password",
-                placeholder="hf_xxxxxxxxx"
-            )
-
-            max_shard_size = gr.Textbox(
-                label="Max Shard Size",
-                value="4.4GB"
-            )
-
-            run_btn = gr.Button(
-                "🚀 Shard & Upload Model",
-                variant="primary"
-            )
-
-        with gr.Column(scale=3):
-
-            logs = gr.Textbox(
-                label="Process Logs",
-                lines=20,
-                interactive=False,
-                autoscroll=True
-            )
-
-    run_btn.click(
-        fn=load_and_reupload_model,
-        inputs=[model_name, new_repo_id, hf_token, max_shard_size],
-        outputs=logs
-    )
-
-
-# =====================================================
-# Launch
-# =====================================================
-
-if __name__ == "__main__":
-    demo.launch()