chore: remove hsare

main.py

@@ -5,7 +5,7 @@ from huggingface_hub import PyTorchModelHubMixin
 from torchvision.models import mobilenet_v3_large
 from torchvision.transforms import v2
 from PIL import Image
-import os
+
 
 
 class TrashMobileNet(nn.Module, PyTorchModelHubMixin):
@@ -24,12 +24,10 @@ class TrashMobileNet(nn.Module, PyTorchModelHubMixin):
         return x
 
 
-# Load the model from Hugging Face Hub
 model_name = "pradanaadn/trash-clasification"
 model = TrashMobileNet.from_pretrained(model_name)
 model.eval()
 
-# Define the image transformations
 transform = v2.Compose([
     v2.Resize((224, 224)),
     v2.ToImage(),
@@ -38,20 +36,18 @@ transform = v2.Compose([
 
 
 def predict(image):
-    """
-    Prediction function that takes a Gradio image input and returns class probabilities
-    """
+
     labels = ["cardboard", "glass", "metal", "paper", "plastic", "trash"]
     
-    # Convert Gradio image to PIL Image if it's not already
+    
     if not isinstance(image, Image.Image):
         image = Image.fromarray(image)
     
-    # Apply transformations and add batch dimension
+    
     image_tensor = transform(image)
     image_tensor = image_tensor.unsqueeze(0)
     
-    # Get model predictions
+   
     with torch.no_grad():
         outputs = model(image_tensor)
         probabilities = torch.nn.functional.softmax(outputs, dim=1)
@@ -63,7 +59,6 @@ def predict(image):
 
 
 
-# Create example images if they don't exist (you would need to provide these images)
 examples = [
     ["examples/cardbox.jpeg", "A cardboard box"],
     ["examples/glass.jpeg", "A glass bottle"],
@@ -106,4 +101,4 @@ with gr.Blocks() as iface:
     
    
 # Launch the interface
-iface.launch(share=True)
+iface.launch()