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| title: Architutor | |
| emoji: 🌍 | |
| colorFrom: yellow | |
| colorTo: yellow | |
| sdk: gradio | |
| sdk_version: 5.48.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: Architecture Feedback Generator | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # Architecture Feedback Generator (Classification & Prompt Only) | |
| This Gradio application performs image and text classification related to architectural design and generates a structured prompt based on the classification results. This version focuses on the classification and prompt generation steps, excluding the interaction with a Large Language Model for generating feedback. | |
| ## Functionality | |
| The application takes two inputs: | |
| 1. **Architectural Image**: Upload an image representing an architectural design. | |
| 2. **Text Description or Question**: Provide a text input related to the architectural design or a question about it. | |
| Based on these inputs, the application performs: | |
| * **Image Classification**: Classifies the image into different architectural design stages (e.g., brainstorm, design iteration, final review). | |
| * **Text Classification**: Determines if the text input contains abstract architectural concepts (High Concept: Yes/No) and provides a confidence score. | |
| The results of both classifications are then used to generate a structured prompt, intended for use with a Large Language Model (LLM). | |
| ## How to Use | |
| 1. Upload an architectural image using the image input box. | |
| 2. Enter your text description or question in the text input box. | |
| 3. Click the "Perform Classification & Generate Prompt" button. | |
| 4. The application will display the Image Classification Results, Text Classification Results, and the Generated Prompt for LLM. | |
| ## Models Used | |
| * **Image Classification Model**: A CNN model hosted on Hugging Face Hub (`keerthikoganti/architecture-design-stages-compact-cnn`). | |
| * **Text Embedding Model**: `sentence-transformers/all-MiniLM-L6-v2` from Hugging Face. | |
| * **Text Classification Model**: An AutoGluon TabularPredictor model hosted on Hugging Face Hub (`kaitongg/my-autogluon-model`), trained on text embeddings. | |
| ## Deployment | |
| This application is designed to be deployed on Hugging Face Spaces. | |
| * **`app.py`**: Contains the complete Gradio application code, including model loading, function definitions, and the Gradio interface. | |
| * **`requirements.txt`**: Lists the necessary Python packages to install for the Space environment. | |
| * **Models**: The models are loaded directly from Hugging Face Hub within the `app.py` file. | |
| To deploy this application: | |
| 1. Create a new Hugging Face Space. | |
| 2. Choose the "Gradio" application template. | |
| 3. Upload the generated `app.py` and `requirements.txt` files to your Space. | |
| 4. Ensure any necessary secrets (like `HF_TOKEN_WRITE` if your text predictor repo is private) are added to your Space settings as environment variables. | |
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