update final deploy

predict.py

@@ -8,7 +8,6 @@ import json
 import os
 import random
 from torchvision import transforms
-from torch.quantization import quantize_dynamic
 
 # Load labels
 with open("labels.json", "r") as f:
@@ -37,25 +36,14 @@ class SwinCustom(nn.Module):
         outputs = self.model(images)
         return outputs.logits
 
-# Download quantized model weights
-model_path = hf_hub_download(repo_id="Noha90/AML_16", filename="swin_large_quantised.pth")
+model_path = hf_hub_download(repo_id="Noha90/AML_16", filename="large_swin_best_model.pth")
 print("Model path:", model_path)
-
-# Build the model 
 model = SwinCustom(model_name=MODEL_NAME, num_classes=40)
-
-# Quantize the model
-quantized_model = quantize_dynamic(model, {nn.Linear}, dtype=torch.qint8)
-
-# Load the quantized state dict
 state_dict = torch.load(model_path, map_location="cpu")
 if "model_state_dict" in state_dict:
     state_dict = state_dict["model_state_dict"]
-quantized_model.load_state_dict(state_dict, strict=False)
-quantized_model.eval()
-
-# Use quantized_model
-model = quantized_model  
+model.load_state_dict(state_dict, strict=False)
+model.eval()
 
 # Preprocessing
 transform = transforms.Compose([