app.py

import warnings
from fastai.vision.all import *
import gradio as gr
import pathlib

# Suppress the pickle warning for demo purposes
warnings.filterwarnings("ignore", category=UserWarning, module="fastai.learner")

# Load the model
learn = load_learner("export.pkl")
labels = learn.dls.vocab

def predict(img):
    """
    Predict skin lesion classification for the given image.
    
    Args:
        img: PIL Image object from Gradio
        
    Returns:
        dict: Classification probabilities for each class
    """
    try:
        # Convert to PILImage if needed
        img = PILImage.create(img)
        pred, pred_idx, probs = learn.predict(img)
        
        # Return as dictionary with float probabilities for JSON serialization
        return {labels[i]: float(probs[i]) for i in range(len(labels))}
    except Exception as e:
        raise gr.Error(f"Error processing image: {str(e)}")

# App metadata
title = "Skin Lesion Classifier [RESNET 50]"
description = "A skin lesion classifier trained on the ISIC2019 dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
article = "<p style='text-align: center'><a href='https://challenge.isic-archive.com/data/' target='_blank'>Link to ISIC Dataset</a></p>"

# Example images
examples = ['img1.jpg', 'img2.jpg', 'img3.jpg'] if all(pathlib.Path(f).exists() for f in ['img1.jpg', 'img2.jpg', 'img3.jpg']) else None

# Create the modern Gradio interface
def create_interface():
    """Create and return the Gradio interface"""
    
    with gr.Blocks(
        title=title,
        theme=gr.themes.Soft(),
        css=".gradio-container {max-width: 700px; margin: auto;}"
    ) as demo:
        
        gr.Markdown(f"# {title}")
        gr.Markdown(description)
        
        with gr.Row():
            with gr.Column():
                image_input = gr.Image(
                    label="Upload Skin Lesion Image",
                    type="pil",
                )
                
                predict_btn = gr.Button(
                    "Classify Lesion", 
                    variant="primary",
                )
            
            with gr.Column():
                output_label = gr.Label(
                    label="Classification Results",
                )
        
        # Add examples if available
        if examples:
            gr.Examples(
                examples=examples,
                inputs=image_input,
                outputs=output_label,
                fn=predict,
                cache_examples=True
            )
        
        # Event handlers
        predict_btn.click(
            fn=predict,
            inputs=image_input,
            outputs=output_label,
            show_progress=True
        )
        
        # Also trigger on image upload
        image_input.upload(
            fn=predict,
            inputs=image_input,
            outputs=output_label,
            show_progress=True
        )
        
        gr.Markdown(article)
    
    return demo

if __name__ == '__main__':
    # Create and launch the interface
    demo = create_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True
    )

















# import gradio as gr
# from fastai.vision.all import *
# import skimage
# #Importing necessary libraries
# import gradio as gr
# #import scikit-learn as sklearn
# from fastai.vision.all import *
# from sklearn.metrics import roc_auc_score

# learn = load_learner('export.pkl')

# labels = learn.dls.vocab
# def predict(img):
#     img = PILImage.create(img)
#     pred,pred_idx,probs = learn.predict(img)
#     return {labels[i]: float(probs[i]) for i in range(len(labels))}


# examples = ['img1.jpg','img2.jpg','img3.jpg']

# #Launching the gradio application
# gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),
#              outputs=gr.outputs.Label(num_top_classes=1),
#              title=title,
#              description=description,article=article,
#              examples=examples,
#              enable_queue=enable_queue).launch(inline=False)

# #gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(224, 224)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()