updated readme

README.md

@@ -10,11 +10,11 @@ pinned: false
 
 # 2D Floorplan Vectorizer
 
-A Streamlit web app that allows you to upload 2D floorplan images and automatically vectorize them into COCO-style annotations using a trained Mask R-CNN model.
+A Gradio web app that allows you to upload 2D floorplan images and automatically vectorize them into COCO-style annotations using a trained Mask R-CNN model. The app runs inside a Docker container and is deployed on HuggingFace Spaces for easy public access. It detects and annotates key elements like rooms, walls, doors, and windows.
 
 ---
 
-## How to Run the App
+## How to Run the App in Local
 
 1. **Clone the repository:**
 
@@ -35,24 +35,26 @@ A Streamlit web app that allows you to upload 2D floorplan images and automatica
     - Place it inside:
 
       ```plaintext
-      inovonics-ui-vectorizer/rcnn_model/output/model_final.pth
+      floorplan-vectorizer/rcnn_model/output/model_final.pth
       ```
 
 4. **Run the app:**
 
     ```bash
-    streamlit run app.py
+    python app.py
     ```
+    - This is the instruction for running the model in local, this will open up the app in localhost.
 
-5. Open your browser at [http://localhost:8501](http://localhost:8501) to start using the app!
+5. Open your browser at [http://localhost:xxxx](http://localhost:xxxx) to start using the app!
 
 ---
 
 ## Project Structure
 
+--- 
 ```plaintext
-inovonics-ui-vectorizer/
-├── app.py                     # Streamlit frontend app
+floorplan-vectorizer/
+├── app.py                     # Gradio frontend app
 ├── public/
 │   └── logo.png                # App logo
 ├── rcnn_model/
@@ -83,3 +85,10 @@ inovonics-ui-vectorizer/
 ├── README.md                   # (this file)
 ├── requirements.txt            # Python dependencies
 └── .gitignore                  # Files to ignore during Git commits
+```
+---
+
+## Huggingface Model
+
+The model is currently running in this [HuggingFace Space](https://huggingface.co/spaces/Dharini27/floorplan-vectorizer) 
+Upload the floorplan image and click on "Run Vectorizer" button, once the model prediction is completed, the floorplan image with detected rooms and the corresponding json will be displayed and ready for download too.

app.py

@@ -90,7 +90,7 @@ with gr.Blocks() as demo:
         with gr.Column():
             uploaded_file = gr.File(label="Upload your Floorplan Image", type="filepath")
             uploaded_image_display = gr.Image(label="Uploaded Image", visible=True)
-            run_button = gr.Button("Run Vectorizer 🔥")
+            run_button = gr.Button("Run Vectorizer")
 
         with gr.Column():
             output_image = gr.Image(label="Output Vectorized Image")