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| # -*- coding: utf-8 -*- | |
| """ | |
| Demo interactiva — Análisis de Sentimientos Multidominio | |
| Modelo: Drocho/distilbert-sentiment | |
| Curso: Deep Learning (1INF52) — PUCP 2026-1 | |
| """ | |
| import gradio as gr | |
| from transformers import pipeline | |
| # Cargar modelo desde Hugging Face | |
| clasificador = pipeline( | |
| "text-classification", | |
| model="Drocho/distilbert-sentiment", | |
| truncation=True, | |
| max_length=512, | |
| ) | |
| LABEL_MAP = { | |
| "NEGATIVE": ("Negativo 😞", "#e74c3c"), | |
| "POSITIVE": ("Positivo 😊", "#2ecc71"), | |
| } | |
| EJEMPLOS = [ | |
| ["This movie was absolutely fantastic! The acting was superb and the story kept me hooked the entire time."], | |
| ["Terrible experience. The product broke after one day and customer service was completely useless."], | |
| ["I can't believe how good this restaurant is. Best meal I've had in years, will definitely come back!"], | |
| ["Waste of money. The film was boring, predictable, and way too long. Don't bother watching it."], | |
| ["The book was okay. Some parts were interesting but overall it felt a bit slow and repetitive."], | |
| ["Amazing service! The staff went above and beyond to make sure we had a great experience."], | |
| ] | |
| def analizar(texto: str): | |
| if not texto or not texto.strip(): | |
| return "⚠️ Por favor ingresa un texto.", "", "" | |
| resultado = clasificador(texto)[0] | |
| label_raw = resultado["label"] | |
| score = resultado["score"] | |
| etiqueta, color = LABEL_MAP.get(label_raw, ("Desconocido", "#95a5a6")) | |
| confianza = f"{score * 100:.1f}%" | |
| barra = "█" * int(score * 20) + "░" * (20 - int(score * 20)) | |
| detalle = f"{barra} {confianza}" | |
| return etiqueta, detalle, color | |
| def wrapper(texto): | |
| etiqueta, detalle, color = analizar(texto) | |
| return etiqueta, detalle | |
| # Interfaz | |
| with gr.Blocks( | |
| title="Análisis de Sentimientos — DistilBERT", | |
| theme=gr.themes.Soft(), | |
| ) as demo: | |
| gr.Markdown( | |
| """ | |
| # 🎭 Análisis de Sentimientos Multidominio | |
| ### Modelo DistilBERT fine-tuneado — PUCP Deep Learning 2026-1 | |
| Clasifica reseñas de películas, productos, restaurantes y más como **positivas** o **negativas**. | |
| > **Modelo:** [`Drocho/distilbert-sentiment`](https://huggingface.co/Drocho/distilbert-sentiment) | |
| > **Dataset:** IMDb · Amazon Polarity · Yelp · SST-2 — 36,496 muestras | |
| > **Accuracy:** 92.85% · **F1-score:** 92.63% | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| texto_input = gr.Textbox( | |
| label="Texto a analizar", | |
| placeholder="Escribe o pega aquí una reseña en inglés...", | |
| lines=5, | |
| max_lines=10, | |
| ) | |
| btn = gr.Button("Analizar sentimiento 🔍", variant="primary") | |
| with gr.Column(scale=1): | |
| sentimiento_output = gr.Textbox( | |
| label="Sentimiento detectado", | |
| interactive=False, | |
| lines=2, | |
| ) | |
| confianza_output = gr.Textbox( | |
| label="Confianza del modelo", | |
| interactive=False, | |
| lines=2, | |
| ) | |
| gr.Markdown("### 📝 Ejemplos") | |
| gr.Examples( | |
| examples=EJEMPLOS, | |
| inputs=texto_input, | |
| outputs=[sentimiento_output, confianza_output], | |
| fn=wrapper, | |
| cache_examples=True, | |
| label="Haz clic en un ejemplo para probarlo", | |
| ) | |
| btn.click( | |
| fn=wrapper, | |
| inputs=texto_input, | |
| outputs=[sentimiento_output, confianza_output], | |
| ) | |
| texto_input.submit( | |
| fn=wrapper, | |
| inputs=texto_input, | |
| outputs=[sentimiento_output, confianza_output], | |
| ) | |
| gr.Markdown( | |
| """ | |
| --- | |
| **Integrantes:** Cáceres Lecaros F. · Ching Sato D. · Condori Chocce Z. · Serpa Paredes C. · Chavez Gonzalez P. | |
| **Limitaciones:** El modelo fue entrenado en inglés. Puede tener dificultades con sarcasmo, | |
| ironía o reseñas con sentimientos mixtos. | |
| """ | |
| ) | |
| demo.launch() |