---
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.

---