deploy the compressed model file

README.md

@@ -12,5 +12,31 @@ pinned: false
 
 # AML 16
 
-This is a Demo using Gradio app for AML 16.
+This is a demo application for the best-performing model (Swin-Large) created for the AML 16 project.  
+The app uses Gradio to provide an interactive interface where users can upload an image, view the top-1 predicted scene category, see a reference image from the predicted class, and explore the top-5 prediction probabilities in a bar chart.
 
+The model was trained for scene classification and deployed using Hugging Face Spaces.
+
+- predict.py
+This file handles loading the trained Swin-Large model and making predictions.
+It loads the model weights from Hugging Face Hub, applies the correct image preprocessing, and outputs:
+
+The uploaded image,
+
+A reference image from the predicted class,
+
+The Top-5 prediction probabilities.
+
+The model was customized with an updated classifier head, and class labels are loaded from a labels.json file. A random sample image from the predicted class folder is also shown for better visualization.
+
+- app.py
+This file builds the Gradio interface.
+It lets users upload an image, runs the prediction using predict.py, and displays:
+
+The uploaded image,
+
+An image for the top-1 predicted class,
+
+The predicted class label,
+
+A bar chart showing the Top-5 prediction probabilities.

predict.py

@@ -36,7 +36,7 @@ class SwinCustom(nn.Module):
         outputs = self.model(images)
         return outputs.logits
 
-model_path = hf_hub_download(repo_id="Noha90/AML_16", filename="large_swin_best_model.pth")
+model_path = hf_hub_download(repo_id="Noha90/AML_16", filename="swin_large_quantised.pth")
 print("Model path:", model_path)
 model = SwinCustom(model_name=MODEL_NAME, num_classes=40)
 state_dict = torch.load(model_path, map_location="cpu")