app.py

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

# Load model and processor
model_id = "sheikh987/Skin_Cancer-Image_Classification"
processor = AutoImageProcessor.from_pretrained(model_id)
model = AutoModelForImageClassification.from_pretrained(model_id)

# Prediction function
def classify_image(img):
    inputs = processor(images=img, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_idx = logits.argmax(-1).item()
    predicted_class = model.config.id2label[predicted_class_idx]
    confidence = torch.nn.functional.softmax(logits, dim=-1)[0][predicted_class_idx].item()
    return {predicted_class: confidence}

# Gradio interface
interface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=3),
    title="Skin Cancer Image Classifier",
    description="Upload an image of skin lesion to classify."
)

interface.launch()