Delete waste_sorting.py

waste_sorting.py

@@ -1,56 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torchvision import transforms
-from PIL import Image
-from transformers import AutoModelForImageClassification, AutoImageProcessor
-import gradio as gr
-
-# Load model and image processor
-model_name = "watersplash/waste-classification"  # Change to a valid model
-model = AutoModelForImageClassification.from_pretrained(model_name)
-image_processor = AutoImageProcessor.from_pretrained(model_name)
-
-# Define preprocessing function
-def preprocess_image(image):
-    transform = transforms.Compose([
-        transforms.Resize((224, 224)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
-    ])
-    return transform(image).unsqueeze(0)  # Add batch dimension
-
-# Define multi-label prediction function
-def predict_waste(image):
-    image = Image.fromarray(image)  # Convert NumPy array to PIL image
-    input_tensor = preprocess_image(image)
-
-    # Get model predictions
-    with torch.no_grad():
-        outputs = model(input_tensor)
-
-    # Apply sigmoid activation for multi-label classification
-    probabilities = torch.sigmoid(outputs.logits)[0]  # Convert logits to probabilities
-
-    # Set a threshold to select labels (e.g., >= 50%)
-    threshold = 0.5
-    predicted_labels = [label for idx, label in model.config.id2label.items() if probabilities[idx] >= threshold]
-    confidence_scores = [f"{probabilities[idx] * 100:.2f}%" for idx in range(len(probabilities)) if probabilities[idx] >= threshold]
-
-    if predicted_labels:
-        result = "\n".join([f"{label}: {score}" for label, score in zip(predicted_labels, confidence_scores)])
-    else:
-        result = "No clear classification (confidence below threshold)"
-
-    return result
-
-# Create Gradio interface
-interface = gr.Interface(
-    fn=predict_waste,
-    inputs=gr.Image(type="numpy"),
-    outputs="text",
-    title="Multi-Label Waste Sorting App",
-    description="Upload an image of waste. The model will classify it into multiple waste categories with confidence scores."
-)
-
-# Launch the app
-interface.launch()