README.md

---
title: AI Based Data Cleaner
emoji: 🚀
colorFrom: red
colorTo: red
sdk: streamlit
app_file: src/streamlit_app.py
app_port: 8501
tags:
- streamlit
pinned: false
short_description: Comprehensive AI-powered data cleaning and validation web ap
license: mit
sdk_version: 1.46.1
---

# 🤗 Hugging Face

[![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://python.org)
[![PyTorch](https://img.shields.io/badge/PyTorch-2.0%2B-red.svg)](https://pytorch.org)
[![TensorFlow](https://img.shields.io/badge/TensorFlow-2.0%2B-orange.svg)](https://tensorflow.org)
[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](LICENSE)

Hugging Face is the AI community building the future. Our platform provides tools, libraries, and resources to discover, collaborate on, and build with state-of-the-art machine learning models.

## 🚀 Features

### 📚 Model Hub
- Access thousands of pre-trained models for NLP, computer vision, audio, and more
- Filter models by task, framework, language, and license
- Community-contributed models with documentation and examples

### 🧠 Transformers Library
- Easy-to-use API for state-of-the-art models (BERT, GPT, T5, LLaMA, etc.)
- Multi-framework support (PyTorch, TensorFlow, JAX)
- Optimized for research and production

### 🔍 Datasets
- Thousands of ready-to-use datasets for various ML tasks
- Standardized access pattern across all datasets
- Efficient data loading and preprocessing

### 🛠️ Spaces
- Interactive ML demos and applications
- Share your models with the community
- Built-in deployment and hosting

## 📋 Installation

### Basic Installation
```bash
pip install transformers
```

### With TensorFlow
```bash
pip install 'transformers[tf-cpu]'
```

### With Flax
```bash
pip install 'transformers[flax]'
```

### For Apple Silicon (M1/ARM)
```bash
# Install prerequisites
brew install cmake
brew install pkg-config

# Then install TensorFlow
pip install 'transformers[tf-cpu]'
```

## 🚀 Quick Start

### Verify Installation
```python
from transformers import pipeline
print(pipeline('sentiment-analysis')('we love you'))
# Output: [{'label': 'POSITIVE', 'score': 0.9998704791069031}]
```

## 🔥 Popular Models

### LLaMA & LLaVA Models
- LLaMA: High-performance foundation models
- LLaVA-NeXT: Improved reasoning, OCR, and world knowledge
- VipLLaVA: Understanding arbitrary visual prompts

### Multimodal Models
- CLIP: Connect images and text
- Stable Diffusion: Generate images from text
- Whisper: Speech recognition and translation

## 🧪 MLX Support
- Native support for Apple silicon
- Efficient model training and serving
- Examples for text generation, fine-tuning, image generation, and speech recognition

## 📊 Example Use Cases

### Text Classification
```python
from transformers import pipeline
classifier = pipeline("sentiment-analysis")
result = classifier("I love working with Hugging Face!")
print(result)
```

### Image Analysis
```python
from transformers import pipeline
image_classifier = pipeline("image-classification")
result = image_classifier("path/to/image.jpg")
print(result)
```

### Multimodal Analysis
```python
# Analyzing artistic styles with multimodal embeddings
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub(
    "huggan/wikiart",
    format="parquet",
    classification_fields=["artist", "style", "genre"],
    max_samples=1000,
    name="wikiart",
)
```

## 📖 Documentation
Visit [huggingface.co/docs](https://huggingface.co/docs) for comprehensive documentation.

## 🤝 Contributing
Join the Hugging Face community to collaborate on models, datasets, and Spaces.

## 📄 License
This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.

---

**Made with ❤️ by the Hugging Face team and community**