README.md

---
title: HelloWorld
emoji: 🌖
colorFrom: gray
colorTo: pink
sdk: gradio
sdk_version: 5.47.2
app_file: app.py
pinned: false
short_description: trial project for HF
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

# Fashion Item Classifier

A Gradio-based web application that classifies fashion items from image URLs using CLIP (Contrastive Language-Image Pre-training) model.

## Steps to Create This Hugging Face Space

Based on the guide from [Marqo's blog post](https://www.marqo.ai/blog/how-to-create-a-hugging-face-space), here are the steps followed:

### 1. Create an Account
- Head to [Hugging Face](https://huggingface.co/) and create an account
- Follow the sign-up process with your details

### 2. Confirm Your Email Address
- Check your email to confirm your account
- This enables access to all Hugging Face features, including Spaces

### 3. Head to Spaces
- After confirming email, log in and click on **Spaces** in the main navigation bar
- This is where you manage and deploy your models and apps

### 4. Create a New Space
- Click **Create New Space**
- Configure the following settings:
  - **Owner**: Your Hugging Face account name
  - **Space name**: Choose a descriptive name (e.g., 'fashion-classifier')
  - **Short Description**: Optional description of your project
  - **License**: Optional
  - **Space SDK**: Select **Gradio**
  - **Gradio template**: Keep as **Blank**
  - **Space hardware**: Use **CPU basic • 2 CPU • 16 GB • FREE** for free tier
  - **Privacy**: Select **Public** to share with others
- Click **Create Space**

### 5. Install Git
- If you don't have Git, download it from [Git's official page](https://git-scm.com/downloads)
- Install Git for your operating system
- Verify installation by running: `git --version`

### 6. Clone the Hugging Face Space
```bash
git clone https://huggingface.co/spaces/your-username/your-space
```
Replace `your-username` and `your-space` with your actual username and space name.

### 7. Open the Folder in VSCode
- Navigate to the cloned folder
- Open it in Visual Studio Code (VSCode)
- Initially, you'll only have `.gitattributes` and `README.md` files

### 8. Create an app.py File
- Create a new file named `app.py` in VSCode
- This contains the main application code for your fashion item classifier

### 9. Add Dependencies
- Create a `requirements.txt` file
- List all required Python packages for your application

### 10. Test Your App Locally
Create a virtual environment and test locally:
```bash
# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run the app
python app.py
```

### 11. Upload to Hugging Face Hub
- Create a `.gitignore` file to exclude unnecessary files (like `venv/`)
- Commit and push your code:
```bash
git add .
git commit -m "Initial commit"
git push origin main
```

## Development Challenges and Solutions

### Problem 1: PyTorch Meta Tensor Error
**Issue**: The original `Marqo/marqo-fashionSigLIP` model encountered a meta tensor error:
```
NotImplementedError: Cannot copy out of meta tensor; no data! 
Please use torch.nn.Module.to_empty() instead of torch.nn.Module.to() 
when moving module from meta to a different device.
```

**Root Cause**: This error occurred due to compatibility issues between the custom SigLIP model and newer versions of PyTorch/transformers. The model was being initialized with meta tensors (tensors without actual data) and the `open_clip` library was trying to move them to a device using the deprecated `.to()` method.

**Attempted Solutions**:
1. **Environment Variables**: Tried setting `PYTORCH_CUDA_ALLOC_CONF` and disabling meta device initialization
2. **Model Parameters**: Attempted using `torch_dtype=torch.float32`, `device_map="cpu"`, and `low_cpu_mem_usage=False`
3. **Accelerate Library**: Installed the `accelerate` library as required by the error messages
4. **PyTorch Version Downgrade**: Attempted to downgrade PyTorch to version 2.1.0 (not available for Windows)

**Final Solution**: Replaced the problematic model with the standard OpenAI CLIP model:
- **Original Model**: `Marqo/marqo-fashionSigLIP` (custom SigLIP implementation)
- **Final Model**: `openai/clip-vit-base-patch32` (standard CLIP model)

### Problem 2: Model Architecture Differences
**Issue**: The code structure needed to be adapted for the different model architecture.

**Solution**: Updated the prediction function to use CLIP's unified text-image processing:
- **Before**: Separate text preprocessing and feature extraction using `get_text_features()` and `get_image_features()`
- **After**: Combined processing using `processor(images=image, text=fashion_items)` and `model(**inputs)`

### Problem 3: Windows Command Compatibility
**Issue**: The original tutorial used Unix/Linux commands (`source venv/bin/activate`) which don't work on Windows PowerShell.

**Solution**: Used Windows-compatible commands:
- **Virtual Environment Activation**: Used direct Python execution via `venv\Scripts\python.exe` instead of activating the environment
- **Package Installation**: `venv\Scripts\python.exe -m pip install -r requirements.txt`

### Final Model Choice: OpenAI CLIP
**Selected Model**: `openai/clip-vit-base-patch32`

**Reasons for Selection**:
1. **Stability**: Well-tested and widely used in production environments
2. **Compatibility**: Full compatibility with current PyTorch and transformers versions
3. **Performance**: Excellent performance on image-text classification tasks
4. **Documentation**: Extensive documentation and community support
5. **Simplicity**: Straightforward implementation without custom code requirements

**Trade-offs**:
- **Specialization**: Less specialized for fashion items compared to the original SigLIP model
- **Accuracy**: May have slightly lower accuracy on fashion-specific classifications
- **Model Size**: Standard CLIP model size vs. potentially optimized SigLIP

The final implementation successfully classifies fashion items into categories: 'top', 'trousers', and 'bottom' using image URLs.