Update README.md

README.md

@@ -12,3 +12,48 @@ 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.
+
+---