Upload Push

.dockerignore

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+# Python
+__pycache__
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+venv/
+ENV/
+.venv
+
+# Docker
+Dockerfile
+.dockerignore
+
+# Git
+.git
+.gitignore
+
+# IDE
+.vscode
+.idea
+*.swp
+*.swo
+*~
+
+# Project specific
+uv.lock
+mobilenet_v2_1.0_224.tflite
+main.py
+README.md
+
+# OS
+.DS_Store
+Thumbs.db
+

.python-version

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+3.12

Dockerfile

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+FROM python:3.13.5-slim
+
+WORKDIR /app
+
+# Install system dependencies required for OpenCV and MediaPipe
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender-dev \
+    libgomp1 \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set Streamlit configuration
+ENV STREAMLIT_BROWSER_GATHER_USAGE_STATS=false 
+
+# Copy requirements and install Python dependencies
+COPY requirements.txt ./
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+# Copy application files
+COPY streamlit_app.py ./
+COPY classifier.tflite ./
+
+# Copy optional images folder if it exists
+COPY images/ ./images/
+
+# Expose Streamlit port
+EXPOSE 8501
+
+# Health check
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+
+# Run the Streamlit app
+ENTRYPOINT ["streamlit", "run", "streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+
+

README.md

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+---
+title: E-Commerce Image Classifier
+emoji: 🛒
+colorFrom: blue
+colorTo: purple
+sdk: docker
+app_port: 8501
+tags:
+ - streamlit
+license: apache-2.0
+---
+
+# E-Commerce Image Classifier 🛒
+
+An intelligent image classification application powered by MediaPipe and Streamlit that automatically categorizes e-commerce product images using a pre-trained deep learning model.
+
+## 🌟 Features
+
+- **Real-time Image Classification**: Upload images and get instant classification results
+- **Batch Processing**: Upload multiple images or entire directories
+- **Interactive Navigation**: Browse through multiple images with intuitive arrow controls
+- **Customizable Results**: Choose how many classification results to display (1-5)
+- **Confidence Scores**: Visual progress bars showing prediction confidence
+- **Modern UI**: Clean, responsive interface optimized for all screen sizes
+- **Pre-loaded Samples**: Default images available for quick testing
+
+## 🚀 Live Demo
+
+Try the live demo on Hugging Face Spaces: [Your Space URL]
+
+## 🎯 How to Use
+
+1. **Upload Images**: 
+   - Choose between "Directory" mode to upload a folder of images
+   - Or "Select Images" mode to pick individual files
+   
+2. **View Results**:
+   - See the original image on the left
+   - Classification results with confidence scores on the right
+   
+3. **Navigate**:
+   - Use arrow buttons (⬅️ ➡️) to browse through multiple images
+   - Current image counter shows your position in the gallery
+
+4. **Customize**:
+   - Adjust "Number of classes to display" in the sidebar (1-5)
+   - View top predictions based on your preference
+
+## 🛠️ Tech Stack
+
+- **Frontend**: Streamlit
+- **ML Framework**: MediaPipe
+- **Computer Vision**: OpenCV
+- **Model**: Pre-trained TFLite classifier (ImageNet 1000 categories)
+- **Language**: Python 3.12+
+
+## 📦 Installation
+
+### Local Setup
+
+```bash
+# Clone the repository
+git clone <your-repo-url>
+cd Image_Classifier
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the application
+streamlit run streamlit_app.py
+```
+
+### Docker Deployment
+
+```bash
+# Build the Docker image
+docker build -t image-classifier .
+
+# Run the container
+docker run -p 8501:8501 image-classifier
+
+# Access at http://localhost:8501
+```
+
+## 🤗 Deploy on Hugging Face Spaces
+
+1. **Create a new Space**:
+   - Go to [Hugging Face Spaces](https://huggingface.co/spaces)
+   - Click "Create new Space"
+   - Select "Streamlit" as the SDK
+
+2. **Upload your files**:
+   - `streamlit_app.py`
+   - `requirements.txt`
+   - `classifier.tflite`
+   - `images/` folder (optional)
+   - `README.md`
+
+3. **Configure**:
+   - The app will automatically deploy using the settings in this README's header
+   - Wait for the build to complete
+   - Your app will be live!
+
+## 📋 Requirements
+
+```
+streamlit>=1.51.0
+opencv-python-headless
+mediapipe>=0.10.21
+```
+
+## ⚠️ Known Limitations
+
+- Uses pre-trained MediaPipe general classifier
+- Limited to 1000 ImageNet categories
+- Not customized for specific product domains
+- Maximum 10MB per image
+- Best results with clear, single-subject images
+
+## 💡 Tips for Best Results
+
+- Use clear, well-lit images
+- Single subject per image works best
+- Avoid ambiguous or complex scenes
+- Common objects and scenes perform better
+- Good focus and resolution recommended
+
+## 🔧 Model Information
+
+- **Model**: MediaPipe Image Classifier
+- **Format**: TensorFlow Lite (.tflite)
+- **Categories**: 1000 ImageNet classes
+- **Input Size**: Variable (automatically resized to 450x300 for display)
+- **Architecture**: MobileNet-based
+
+## 📄 License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## 🙏 Credits
+
+**Developed by [Techtics.ai](https://techtics.ai)**
+
+Built with:
+- [MediaPipe](https://mediapipe.dev/) by Google
+- [Streamlit](https://streamlit.io/)
+- [OpenCV](https://opencv.org/)
+
+## 🐛 Issues and Contributions
+
+Found a bug or want to contribute? Please open an issue or submit a pull request on [GitHub](https://github.com/travelmateen/image-classification-ecommerce).
+
+## 📧 Contact
+
+For questions or collaborations, visit [Techtics.ai](https://techtics.ai)
+
+---
+
+Made with ❤️ by Techtics.ai
+

pyproject.toml

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+[project]
+name = "image-classifier"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "mediapipe>=0.10.21",
+    "streamlit>=1.51.0",
+]

streamlit_app.py

@@ -45,7 +45,7 @@ st.title("E-Commerce Image Classifier")
 st.write(
     "Try uploading an image or a folder to see automatic classification results. "
     "You can navigate between images using the arrow buttons below. "
-    "This project is open source — check it out on [GitHub](https://github.com/travelmateen/ImageClassifier). 🚀"
+    "This project is open source — check it out on [GitHub](https://github.com/travelmateen/image-classification-ecommerce). 🚀"
 )
 st.markdown("<style> div[data-testid='stStatusWidget']{display:none}</style>", unsafe_allow_html=True)