app.py

import random
import gradio as gr
from PIL import Image
from model import predict
from datasets import load_dataset

dataset = load_dataset("AIOmarRehan/AnimalsDataset", split="train")

def classify_image(img: Image.Image):
    if img is None:
        return "No image uploaded", 0, {}
    
    label, confidence, probs = predict(img)
    return (
        label,
        round(confidence, 3),
        {k: round(v, 3) for k, v in probs.items()}
    )

# Random example from the dataset
def random_example():
    item = random.choice(dataset)
    img = item["image"].convert("RGB")
    label = dataset.features["label"].int2str(item["label"])
    # Return image twice: once for input_img (for prediction), once for display
    return img, img, label

# Gradio UI
demo = gr.Blocks()

with demo:
    gr.Markdown("## Animal Image Classifier with Random Dataset Samples")
    
    with gr.Row():
        input_img = gr.Image(type="pil", label="Upload an image")
        rand_img = gr.Button("Random Dataset Image")
    
    pred_btn = gr.Button("Predict")
    
    output_label = gr.Label(label="Predicted Class")
    output_conf = gr.Number(label="Confidence")
    output_probs = gr.JSON(label="All Probabilities")
    
    rand_display = gr.Image(type="pil", label="Random Dataset Sample")
    rand_label = gr.Textbox(label="Sample Label")
    
    # Predict button uses whatever image is currently in input_img
    pred_btn.click(
        classify_image,
        inputs=input_img,
        outputs=[output_label, output_conf, output_probs]
    )

    # Random button picks a dataset image
    rand_img.click(
        random_example,
        outputs=[input_img, rand_display, rand_label]
    )

if __name__ == "__main__":
    demo.launch()