app.py

import gradio as gr
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification

# Load the pre-trained model and image processor
processor = AutoImageProcessor.from_pretrained("tiya1012/vit-accident-image")
model = AutoModelForImageClassification.from_pretrained("tiya1012/vit-accident-image")

# Define a label mapping for `LABEL_0` and `LABEL_1`
label_mapping = {
    "LABEL_0": "No Accident",
    "LABEL_1": "Accident Detected"
}

# Define the classification function
def classify_accident_image(image):
    # Ensure the image is provided
    if image is None:
        return "No image uploaded"
    
    # Preprocess the image
    inputs = processor(images=image, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits

    # Debug: Print logits for analysis
    print("Logits:", logits)

    # Get the predicted class index and label
    probabilities = torch.softmax(logits, dim=1)[0]  # Softmax to get probabilities
    predicted_class_idx = torch.argmax(probabilities).item()
    print("Predicted Class Index:", predicted_class_idx)
    print("Probabilities:", probabilities)

    # Map the model's label to human-readable label using label_mapping
    predicted_label_key = model.config.id2label[predicted_class_idx]
    predicted_label = label_mapping.get(predicted_label_key, "Unknown")

    # Get the confidence score
    confidence = probabilities[predicted_class_idx].item() * 100

    # Format the result
    result = f"Prediction: {predicted_label}\nConfidence: {confidence:.2f}%"
    
    return result

# Create Gradio interface
iface = gr.Interface(
    fn=classify_accident_image,
    inputs=gr.Image(type="pil", label="Upload Accident Image"),
    outputs=gr.Textbox(label="Classification Result"),
    title="Accident Image Classifier",
    description="Upload an image to classify whether it depicts an accident or not.",
)

# Launch the interface
if __name__ == "__main__":
    iface.launch()