Initial commit

app.py

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+import torch
+import torch.nn as nn
+from torchvision import transforms
+from transformers import CLIPModel
+from PIL import Image
+import gradio as gr
+
+# Define class labels
+class_labels = ["Trash", "Compostable", "Recyclable"]
+
+# Define CLIP Classifier (same as used during training)
+class CLIPClassifier(nn.Module):
+    def __init__(self, clip_model, num_classes):
+        super(CLIPClassifier, self).__init__()
+        self.clip = clip_model.vision_model
+        self.fc = nn.Linear(768, num_classes)
+
+    def forward(self, images):
+        image_features = self.clip(images).pooler_output
+        return self.fc(image_features)
+
+# Load the model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+model = CLIPClassifier(clip_model, num_classes=3).to(device)
+
+# Load the saved weights
+model.load_state_dict(torch.load("clip_trash_classifier_finetuned.pth", map_location=device))
+model.eval()
+
+# Preprocessing pipeline
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],  # CLIP's mean
+                         std=[0.26862954, 0.26130258, 0.27577711])  # CLIP's std
+])
+
+# Prediction function
+def predict(image):
+    """
+    Function to predict the class label and confidence of the uploaded image.
+    Returns separate values for label and confidence.
+    """
+    # Preprocess the image
+    image = transform(image).unsqueeze(0).to(device)
+
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(image)
+        probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
+        confidence, predicted = torch.max(probabilities, dim=0)
+
+    # Get predicted class and confidence score
+    predicted_class = class_labels[predicted.item()]
+    confidence_score = f"{confidence.item() * 100:.2f}%"
+
+    # Return as separate outputs
+    return predicted_class, confidence_score
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict,                    # Prediction function
+    inputs=gr.Image(type="pil"),   # Input: Image in PIL format
+    outputs=[
+        gr.Textbox(label="Predicted Category"),  # Output 1: Predicted Label
+        gr.Textbox(label="Confidence")           # Output 2: Confidence Score
+    ],
+    title="Trash Classifier Using CLIP",
+    description="Upload an image to classify it as **Trash**, **Compostable**, or **Recyclable**.\n"
+                "The app will display the predicted category and confidence score."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch(share=True)

clip_trash_classifier_finetuned.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3091e4fd097fbca183acb6aa0a21115fd359d1c40b15601adde1a9ab2c2072d0
+size 349913432

requirements.txt

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+torch==2.0.1  
+torchvision==0.15.2
+transformers==4.31.0
+gradio==3.50.2
+Pillow==10.0.0