app.py

import gradio as gr
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import torch

# Cargar modelo y extractor
model = ViTForImageClassification.from_pretrained("akahana/vit-base-cats-vs-dogs")
feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")

# Función de predicción
def classify_image(image):
    inputs = feature_extractor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_idx = logits.argmax(-1).item()
    predicted_class = model.config.id2label[predicted_class_idx]
    return predicted_class

# Interfaz mejorada
with gr.Blocks() as demo:
    gr.Markdown("# 🐱🐶 Clasificador de Gatos vs Perros")
    gr.Markdown("Sube una imagen de un gato o un perro. Este modelo basado en Vision Transformer (ViT) te dirá cuál es.")

    with gr.Row():
        with gr.Column():
            image_input = gr.Image(label="📷 Sube tu imagen", type="pil")
            submit_btn = gr.Button("🔍 Clasificar")
        with gr.Column():
            output_label = gr.Textbox(label="🔎 Resultado", interactive=False)

    submit_btn.click(fn=classify_image, inputs=image_input, outputs=output_label)

demo.launch()