Delete augmented_waste_classifier.py

augmented_waste_classifier.py

@@ -1,45 +0,0 @@
-import gradio as gr
-import spaces
-from transformers import AutoImageProcessor
-from transformers import SiglipForImageClassification
-from transformers.image_utils import load_image
-from PIL import Image
-import torch
-
-# Load model and processor
-model_name = "prithivMLmods/Augmented-Waste-Classifier-SigLIP2"
-model = SiglipForImageClassification.from_pretrained(model_name)
-processor = AutoImageProcessor.from_pretrained(model_name)
-
-@spaces.GPU
-def waste_classification(image):
-    """Predicts waste classification for an image."""
-    image = Image.fromarray(image).convert("RGB")
-    inputs = processor(images=image, return_tensors="pt")
-    
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits
-        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
-    
-    labels = {
-        "0": "Battery", "1": "Biological", "2": "Cardboard", "3": "Clothes", 
-        "4": "Glass", "5": "Metal", "6": "Paper", "7": "Plastic", 
-        "8": "Shoes", "9": "Trash"
-    }
-    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
-    
-    return predictions
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=waste_classification,
-    inputs=gr.Image(type="numpy"),
-    outputs=gr.Label(label="Prediction Scores"),
-    title="Augmented Waste Classification",
-    description="Upload an image to classify the type of waste."
-)
-
-# Launch the app
-if __name__ == "__main__":
-    iface.launch()