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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()
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