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| import gradio as gr | |
| from transformers import pipeline | |
| from PIL import Image | |
| # Cargamos el modelo CLIP | |
| clf = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32") | |
| def dog_cat_classifier(image): | |
| if image is None: | |
| return {"❌ Sin imagen": 1.0} | |
| image = image.convert("RGB") | |
| candidate_labels = ["dog", "cat", "neither dog nor cat"] | |
| preds = clf(image, candidate_labels=candidate_labels) | |
| result = {} | |
| for p in preds: | |
| label = p["label"] | |
| score = p["score"] | |
| if label == "dog": | |
| result["🐶 Dog"] = score | |
| elif label == "cat": | |
| result["🐱 Cat"] = score | |
| else: | |
| result["❌ Other"] = score | |
| return result | |
| # --- DISEÑO DE LA INTERFAZ --- | |
| with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate")) as demo: | |
| # Encabezado con Estilo | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown( | |
| """ | |
| # 🐾 AI Pet Detector | |
| ### Clasificador inteligente basado en CLIP (Zero-Shot) | |
| Sube una foto y deja que la IA identifique si es un gato, un perro o algo totalmente distinto. | |
| """ | |
| ) | |
| gr.HTML("<hr>") # Una línea separadora elegante | |
| with gr.Row(): | |
| # Columna Izquierda: Entrada | |
| with gr.Column(variant="panel"): | |
| gr.Markdown("### 📸 Paso 1: Sube tu imagen") | |
| input_img = gr.Image(type="pil", label=None) | |
| btn = gr.Button("🚀 Analizar Mascota", variant="primary") | |
| # Columna Derecha: Salida | |
| with gr.Column(variant="panel"): | |
| gr.Markdown("### 🐾 Paso 2: Resultado") | |
| output_label = gr.Label(num_top_classes=3, label="Probabilidades") | |
| # Ejemplos (Añaden mucha "presencia" visual) | |
| gr.Examples( | |
| examples=[["ejemplo_perro.jpg"], ["ejemplo_gato.jpg"]], # Asegúrate de tener estas imágenes o quita esta parte | |
| inputs=input_img | |
| ) | |
| # Pie de página | |
| gr.Markdown( | |
| """ | |
| --- | |
| *Desarrollado con ❤️ usando OpenAI CLIP y Gradio.* | |
| """ | |
| ) | |
| # Evento de clic | |
| btn.click(fn=dog_cat_classifier, inputs=input_img, outputs=output_label) | |
| if __name__ == "__main__": | |
| demo.launch() |