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| title: Food Image Crowdsourcing | |
| emoji: 🍽️ | |
| colorFrom: green | |
| colorTo: yellow | |
| sdk: streamlit | |
| sdk_version: 1.43.2 | |
| app_file: app.py | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # 🍽️ Food Image Crowdsourcing App | |
| A Streamlit application that allows users to upload food images for AI model training. These images will be used to fine-tune datasets for food image recognition to annotate uploaded food pictures with caloric and nutrient values. | |
| ## 📋 Features | |
| - User authentication via Firebase | |
| - Image upload and processing | |
| - Token reward system for contributors | |
| - AWS S3 storage for images | |
| - DynamoDB for metadata storage | |
| ## 🚀 Deployment to Hugging Face Spaces | |
| ### Prerequisites | |
| 1. [Hugging Face](https://huggingface.co/) account | |
| 2. AWS account with S3 and DynamoDB | |
| 3. Firebase project for authentication | |
| ### Step 1: Set up AWS Resources | |
| 1. Create an S3 bucket: `food-image-crowdsourcing` | |
| 2. Create a DynamoDB table: `image_metadata` with primary key `image_id` | |
| 3. Create an IAM user with permissions for S3 and DynamoDB | |
| 4. Note your AWS Access Key and Secret Key | |
| ### Step 2: Set up Firebase Authentication | |
| 1. Create a new Firebase project at [Firebase Console](https://console.firebase.google.com/) | |
| 2. Enable Email/Password authentication | |
| 3. Generate a new private key for your service account (Project settings > Service accounts > Generate new private key) | |
| 4. Save the JSON file | |
| ### Step 3: Create a New Hugging Face Space | |
| 1. Go to [Hugging Face Spaces](https://huggingface.co/spaces) | |
| 2. Click "Create new Space" | |
| 3. Choose a name for your Space | |
| 4. Select "Streamlit" as the SDK | |
| 5. Choose visibility (Public or Private) | |
| 6. Click "Create Space" | |
| ### Step 4: Upload Files to the Space | |
| Upload the following files to your Space: | |
| - `app.py` | |
| - `requirements.txt` | |
| - `PARTICIPATION_GUIDELINES.md` | |
| - `TOKEN_REWARDS.md` | |
| - `TERMS_OF_SERVICE.md` | |
| You can do this via the Hugging Face web interface or using Git. | |
| ### Step 5: Configure Secrets | |
| 1. Go to your Space's Settings tab | |
| 2. Under "Repository secrets", add the following secrets: | |
| - `AWS_ACCESS_KEY`: Your AWS access key | |
| - `AWS_SECRET_KEY`: Your AWS secret key | |
| - `AWS_REGION`: Your AWS region (e.g., `us-east-1`) | |
| - `S3_BUCKET_NAME`: Your S3 bucket name (e.g., `food-image-crowdsourcing`) | |
| - `DYNAMODB_TABLE`: Your DynamoDB table name (e.g., `image_metadata`) | |
| - `FIREBASE_CONFIG`: Paste the entire content of your Firebase service account JSON file | |
| ### Step 6: Deploy and Run | |
| 1. Your Space should automatically build and deploy when you push your changes | |
| 2. If not, you can manually rebuild from the Settings tab | |
| 3. Once deployed, you can access your app at `https://huggingface.co/spaces/your-username/your-space-name` | |
| ## 📄 Important Files | |
| - `app.py`: The main Streamlit application | |
| - `requirements.txt`: Python dependencies | |
| - `PARTICIPATION_GUIDELINES.md`: Rules for user participation | |
| - `TOKEN_REWARDS.md`: Explanation of the token reward system | |
| - `TERMS_OF_SERVICE.md`: Legal terms and conditions | |
| ## 🔄 Local Development | |
| To run this application locally: | |
| 1. Clone the repository | |
| 2. Install dependencies: `pip install -r requirements.txt` | |
| 3. Create a `.env` file with the required environment variables | |
| 4. Run the app: `streamlit run app.py` | |
| ## 📦 Required Environment Variables | |
| ``` | |
| AWS_ACCESS_KEY=your_aws_access_key | |
| AWS_SECRET_KEY=your_aws_secret_key | |
| AWS_REGION=your_aws_region | |
| S3_BUCKET_NAME=your_s3_bucket_name | |
| DYNAMODB_TABLE=your_dynamodb_table_name | |
| FIREBASE_CONFIG=your_firebase_json_config | |
| ``` | |
| ## 📊 Data Model | |
| ### S3 Object Structure | |
| Images are stored in S3 with the following path pattern: | |
| ``` | |
| {user_id}/{timestamp}_{image_id}.jpg | |
| ``` | |
| ### DynamoDB Schema | |
| Each image entry in DynamoDB contains: | |
| - `image_id` (Primary Key): UUID for the image | |
| - `user_id`: ID of the uploading user | |
| - `upload_timestamp`: When the image was uploaded | |
| - `food_name`: Name of the food | |
| - `portion_size`: Size/weight of the portion | |
| - `portion_unit`: Unit of measurement | |
| - `cooking_method`: How the food was prepared | |
| - `ingredients`: List of main ingredients | |
| - `s3_path`: Path to the image in S3 | |
| - `tokens_awarded`: Number of tokens awarded for this upload | |
| ## 🛠️ Future Enhancements | |
| 1. Image tagging with machine learning | |
| 2. Nutritional information estimation | |
| 3. Social sharing features | |
| 4. Mobile app integration | |
| 5. Enhanced analytics dashboard | |
| ## 📝 License | |
| This project is licensed under the MIT License - see the LICENSE file for details. |