Spaces:
Sleeping
Sleeping
File size: 2,932 Bytes
770db19 839dc8e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | ---
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.
---
|