model.py

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

# Load dataset (NO streaming → allows len() and indexing)
dataset = load_dataset("AIOmarRehan/AnimalsDataset", split="train")

def classify_image(img: Image.Image):

    # Handle empty input safely
    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()}
    )

# Pick a random example
def random_example():

    idx = random.randint(0, len(dataset) - 1)
    item = dataset[idx]

    img = item["image"].convert("RGB")
    label = item["label"]
    label_str = dataset.features["label"].int2str(label)

    return img, label_str


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")
    
    with gr.Row():
        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")
    
    # Display random dataset sample
    rand_display = gr.Image(type="pil", label="Random Dataset Sample")
    rand_label = gr.Textbox(label="Sample Label")
    
    # Actions
    pred_btn.click(
        classify_image,
        inputs=input_img,
        outputs=[output_label, output_conf, output_probs]
    )

    rand_img.click(
        random_example,
        outputs=[rand_display, rand_label]
    )

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