save

app.py

@@ -12,10 +12,55 @@ from transformers import (
     AutoModel
 )
 import torch
+import time
+import threading
+from typing import Callable, Optional
 
 # --- Helper Functions ---
 
 
+class ProgressTracker:
+    """Class to track progress and send updates to the UI"""
+
+    def __init__(self):
+        self.current_stage = 0
+        self.total_stages = 5  # Load model, Get recipe, Run compression, Create repo, Create model card
+        self.stage_descriptions = [
+            "Loading model and tokenizer...",
+            "Preparing quantization recipe...",
+            "Running quantization compression...",
+            "Creating Hugging Face repository and uploading...",
+            "Generating model card..."
+        ]
+        self.progress = 0.0
+        self.status = ""
+        self.lock = threading.Lock()
+
+    def update_stage(self, stage_idx: int, description: str = ""):
+        with self.lock:
+            self.current_stage = stage_idx
+            self.status = description or self.stage_descriptions[stage_idx]
+            # Calculate progress (each stage is 20% of total)
+            self.progress = min(100.0, (stage_idx / self.total_stages) * 100)
+
+    def update_progress(self, current: float, total: float, description: str = ""):
+        with self.lock:
+            # Calculate progress within the current stage
+            stage_progress = (current / total) * (100.0 / self.total_stages)
+            self.progress = min(100.0, ((self.current_stage / self.total_stages) * 100) + stage_progress)
+            if description:
+                self.status = description
+
+    def get_state(self):
+        with self.lock:
+            return {
+                "progress": self.progress,
+                "status": self.status,
+                "current_stage": self.current_stage + 1,  # 1-indexed for display
+                "total_stages": self.total_stages
+            }
+
+
 def get_quantization_recipe(method, model_architecture):
     """
     Returns the appropriate llm-compressor recipe based on the selected method.
@@ -288,6 +333,7 @@ def compress_and_upload(
     quant_method: str,
     model_type_selection: str,  # New parameter for manual model type selection
     oauth_token: gr.OAuthToken | None,
+    progress=gr.Progress()  # Gradio progress tracker
 ):
     """
     Compresses a model using llm-compressor and uploads it to a new HF repo.
@@ -305,39 +351,52 @@ def compress_and_upload(
         username = whoami(token=token)["name"]
 
         # --- 1. Load Model and Tokenizer ---
+        progress(0, desc="Stage 1/5: Loading model and tokenizer...")
         # Determine the appropriate model class based on the model's configuration or user selection
         model_class = determine_model_class(model_id, token, model_type_selection)
 
         try:
+            # Show sub-steps during model loading
+            progress(0.05, desc="Stage 1/5: Determining model class...")
             model = model_class.from_pretrained(
                 model_id, torch_dtype="auto", device_map=None, token=token, trust_remote_code=True
             )
+            progress(0.15, desc="Stage 1/5: Model loaded, loading tokenizer...")
         except ValueError as e:
             if "Unrecognized configuration class" in str(e):
                 # If automatic detection fails, fall back to AutoModel and let transformers handle it
                 print(f"Automatic model class detection failed, falling back to AutoModel: {e}")
+                progress(0.05, desc="Stage 1/5: Using fallback model class...")
                 model = AutoModel.from_pretrained(
                     model_id, torch_dtype="auto", device_map=None, token=token, trust_remote_code=True
                 )
+                progress(0.15, desc="Stage 1/5: Model loaded with fallback class...")
             else:
                 raise
 
         output_dir = f"{model_id.split('/')[-1]}-{quant_method}"
 
         # --- 2. Get Recipe ---
+        progress(0.2, desc="Stage 2/5: Preparing quantization recipe...")
         if not model.config.architectures:
             raise gr.Error("Could not determine model architecture.")
+
+        progress(0.25, desc="Stage 2/5: Analyzing model architecture...")
         recipe = get_quantization_recipe(quant_method, model.config.architectures[0])
+        progress(0.3, desc="Stage 2/5: Quantization recipe prepared!")
 
         # --- 3. Run Compression ---
+        progress(0.35, desc="Stage 3/5: Setting up quantization dataset...")
         # Determine if this is a Qwen2.5-VL model to use appropriate dataset and data collator
         if model.config.architectures and "Qwen2_5_VLForConditionalGeneration" in model.config.architectures[0]:
             # Use a multimodal dataset and data collator for Qwen2.5-VL models
             try:
                 from datasets import load_dataset
+                progress(0.36, desc="Stage 3/5: Loading multimodal dataset for Qwen2.5-VL model...")
                 # Use a small subset of flickr30k for calibration if available
                 ds = load_dataset("lmms-lab/flickr30k", split="test[:64]")
                 ds = ds.shuffle(seed=42)
+                progress(0.38, desc="Stage 3/5: Dataset loaded, preparing data collator...")
 
                 # Define a data collator for multimodal inputs
                 def qwen2_5_vl_data_collator(batch):
@@ -345,6 +404,7 @@ def compress_and_upload(
                     return {key: torch.tensor(value) if isinstance(value, (list, int, float)) else value
                             for key, value in batch[0].items()}
 
+                progress(0.4, desc="Stage 3/5: Starting quantization process for Qwen2.5-VL model...")
                 oneshot(
                     model=model,
                     dataset=ds,
@@ -355,11 +415,14 @@ def compress_and_upload(
                     num_calibration_samples=64,
                     data_collator=qwen2_5_vl_data_collator,
                 )
+                progress(0.7, desc="Stage 3/5: Qwen2.5-VL quantization completed!")
             except Exception as e:
                 print(f"Could not load multimodal dataset, falling back to text-only: {e}")
+                progress(0.36, desc="Stage 3/5: Multimodal dataset failed, using fallback dataset...")
                 # Fall back to text-only dataset - load it properly and pass as dataset
                 from datasets import load_dataset
                 fallback_ds = load_dataset("wikitext", "wikitext-2-raw-v1", split="train[:1%]")
+                progress(0.4, desc="Stage 3/5: Running quantization with fallback dataset...")
                 oneshot(
                     model=model,
                     dataset=fallback_ds,
@@ -369,10 +432,13 @@ def compress_and_upload(
                     max_seq_length=512,
                     num_calibration_samples=64,
                 )
+                progress(0.7, desc="Stage 3/5: Quantization with fallback dataset completed!")
         else:
             # For non-multimodal models, use the original approach
             from datasets import load_dataset
+            progress(0.36, desc="Stage 3/5: Loading text dataset...")
             ds = load_dataset("wikitext", "wikitext-2-raw-v1", split="train[:1%]")
+            progress(0.4, desc="Stage 3/5: Starting quantization process for standard model...")
             oneshot(
                 model=model,
                 dataset=ds,
@@ -382,20 +448,26 @@ def compress_and_upload(
                 max_seq_length=512,
                 num_calibration_samples=64,
             )
+            progress(0.7, desc="Stage 3/5: Quantization completed!")
 
         # --- 4. Create Repo and Upload ---
+        progress(0.75, desc="Stage 4/5: Preparing Hugging Face repository...")
         api = HfApi(token=token)
         repo_id = f"{username}/{output_dir}"
 
+        progress(0.78, desc="Stage 4/5: Creating repository...")
         repo_url = api.create_repo(repo_id=repo_id, exist_ok=True)
 
+        progress(0.8, desc="Stage 4/5: Uploading model files...")
         api.upload_folder(
             folder_path=output_dir,
             repo_id=repo_id,
             commit_message=f"Upload {quant_method} compressed model",
         )
+        progress(0.9, desc="Stage 4/5: Upload completed!")
 
         # --- 5. Create Model Card ---
+        progress(0.95, desc="Stage 5/5: Generating model card...")
         card_content = f"""
 ---
 license: apache-2.0
@@ -419,6 +491,7 @@ For more details on the recipe used, refer to the `recipe.yaml` file in this rep
         card = ModelCard(card_content)
         card.push_to_hub(repo_id, token=token)
 
+        progress(1.0, desc="✅ All stages completed! Your compressed model is ready.")
         return f'<h1>✅ Success!</h1><br/>Model compressed and saved to your new repo: <a href="{repo_url}" target="_blank" style="text-decoration:underline">{repo_id}</a>'
 
     except gr.Error as e:
@@ -428,7 +501,6 @@ For more details on the recipe used, refer to the `recipe.yaml` file in this rep
         return f'<h1>❌ ERROR</h1><br/><pre style="white-space:pre-wrap;">{error_message}</pre>'
 
 
-
 # --- Gradio Interface ---
 def build_gradio_app():
     with gr.Blocks(css="footer {display: none !important;}") as demo:
@@ -479,10 +551,25 @@ def build_gradio_app():
         compress_button = gr.Button("Compress and Create Repo", variant="primary")
         output_html = gr.HTML(label="Result")
 
-        compress_button.click(
+        # Create the event handler with updates to disable button during processing
+        btn_click = compress_button.click(
             fn=compress_and_upload,
             inputs=[model_input, quant_method_dropdown, model_type_dropdown],
             outputs=output_html,
+            show_progress=True  # Show built-in progress bar
+        )
+
+        # Disable button during processing then re-enable it afterward
+        btn_click.then(
+            fn=lambda: gr.Button(interactive=False, value="Processing..."),
+            inputs=[],
+            outputs=[compress_button],
+            queue=False
+        ).then(
+            fn=lambda: gr.Button(interactive=True, value="Compress and Create Repo"),
+            inputs=[],
+            outputs=[compress_button],
+            queue=False
         )
     return demo
 

pyproject.toml

@@ -3,10 +3,11 @@ name = "llm-compressor-my-repo"
 version = "0.1.0"
 requires-python = ">=3.13"
 dependencies = [
-    "gradio>=5.50.0",
     "gradio-huggingfacehub-search>=0.0.12",
+    "gradio[oauth]>=5.50.0",
     "llmcompressor>=0.8.1",
     "torch<=2.8.0",
+    "torchvision<=2.8.0",
 ]
 
 #[tool.uv.sources]

requirements.txt

@@ -8,3 +8,5 @@ llmcompressor==0.8.1
     # via llm-compressor-my-repo (pyproject.toml)
 torch==2.8.0
     # via llm-compressor-my-repo (pyproject.toml)
+torchvision==0.24.1
+    # via llm-compressor-my-repo (pyproject.toml)

uv.lock

@@ -213,6 +213,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f6/22/91616fe707a5c5510de2cac9b046a30defe7007ba8a0c04f9c08f27df312/audioop_lts-0.2.2-cp314-cp314t-win_arm64.whl", hash = "sha256:b492c3b040153e68b9fdaff5913305aaaba5bb433d8a7f73d5cf6a64ed3cc1dd", size = 25206, upload-time = "2025-08-05T16:43:16.444Z" },
 ]
 
+[[package]]
+name = "authlib"
+version = "1.6.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/cd/3f/1d3bbd0bf23bdd99276d4def22f29c27a914067b4cf66f753ff9b8bbd0f3/authlib-1.6.5.tar.gz", hash = "sha256:6aaf9c79b7cc96c900f0b284061691c5d4e61221640a948fe690b556a6d6d10b", size = 164553, upload-time = "2025-10-02T13:36:09.489Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f8/aa/5082412d1ee302e9e7d80b6949bc4d2a8fa1149aaab610c5fc24709605d6/authlib-1.6.5-py2.py3-none-any.whl", hash = "sha256:3e0e0507807f842b02175507bdee8957a1d5707fd4afb17c32fb43fee90b6e3a", size = 243608, upload-time = "2025-10-02T13:36:07.637Z" },
+]
+
 [[package]]
 name = "brotli"
 version = "1.2.0"
@@ -250,6 +262,51 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/70/7d/9bc192684cea499815ff478dfcdc13835ddf401365057044fb721ec6bddb/certifi-2025.11.12-py3-none-any.whl", hash = "sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b", size = 159438, upload-time = "2025-11-12T02:54:49.735Z" },
 ]
 
+[[package]]
+name = "cffi"
+version = "2.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pycparser", marker = "implementation_name != 'PyPy'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" }
+wheels = [
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@@ -667,18 +795,20 @@ name = "llm-compressor-my-repo"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
-    { name = "gradio" },
+    { name = "gradio", extra = ["oauth"] },
     { name = "gradio-huggingfacehub-search" },
     { name = "llmcompressor" },
     { name = "torch" },
+    { name = "torchvision" },
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     { name = "torch", specifier = "<=2.8.0" },
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