app.py

import gradio as gr
from transformers import pipeline
import os

# Añade esto para verificar la versión de Gradio en tiempo de ejecución
print(f"Gradio version at runtime: {gr.__version__}")

# --- Model Loading ---
MODEL_ID = "Light-Dav/sentiment-analysis-full-project"

try:
    # Esto carga tu modelo pre-entrenado desde Hugging Face Hub
    # top_k=None asegura que se devuelvan las puntuaciones de todas las clases (positivo, negativo, neutral)
    sentiment_analyzer = pipeline("sentiment-analysis", model=MODEL_ID, top_k=None)
    model_loaded_successfully = True
    print("Sentiment analysis model loaded successfully.")
except Exception as e:
    print(f"Error loading model: {e}")
    sentiment_analyzer = None
    model_loaded_successfully = False
    print("Sentiment analysis model failed to load. Please check MODEL_ID and network connection.")

# --- Custom CSS for a dark look inspired by the website ---
# Este CSS define todo el aspecto visual sin depender de un tema de Gradio
custom_css = """
body {
    background-color: #121212; /* Dark background */
    color: #f8f8f2; /* Light text */
}
.gradio-container {
    box-shadow: 0 4px 8px rgba(255, 255, 255, 0.1);
    border-radius: 10px;
    overflow: hidden;
    background-color: #1e1e1e; /* Darker card background */
    padding: 20px;
    margin-bottom: 20px;
}
h1, h2, h3 {
    color: #80cbc4; /* Teal/Cyan accents */
    text-align: center;
    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
    animation: fadeIn 1s ease-in-out;
}
.gr-button.primary {
    background-color: #80cbc4 !important;
    color: #1e1e1e !important;
    border-radius: 6px;
    transition: background-color 0.3s ease;
    padding: 10px 20px;
}
.gr-button.primary:hover {
    background-color: #26a69a !important;
}
.gradio-output {
    border: 1px solid #424242;
    border-radius: 8px;
    padding: 15px;
    margin-top: 15px;
    background-color: #212121;
    color: #f8f8f2;
}
.sentiment-display {
    text-align: center;
    padding: 10px;
    border-radius: 6px;
    margin-top: 10px;
    font-size: 1.1em;
    font-weight: bold;
}
.sentiment-positive {
    background-color: #388e3c; /* Darker green */
    color: #e8f5e9; /* Light green */
}
.sentiment-negative {
    background-color: #d32f2f; /* Darker red */
    color: #ffebee; /* Light red */
}
.sentiment-neutral {
    background-color: #1976d2; /* Darker blue */
    color: #e3f2fd; /* Light blue */
}
@keyframes fadeIn {
    from { opacity: 0; }
    to { opacity: 1; }
}
/* Estilos para las etiquetas de los componentes de entrada */
gr-textbox > label {
    color: #80cbc4;
}
/* Asegúrate de que las etiquetas de salida también tengan color */
.gradio-output .label {
    color: #80cbc4; /* Color de acento para las etiquetas de salida */
}
"""

# --- Helper Function for Sentiment Interpretation ---
def interpret_sentiment(label, score):
    emoji = ""
    description = ""
    color_class = ""

    if label.lower() == "positive" or label.lower() == "label_2":
        emoji = "😊"
        description = "This text expresses a **highly positive** sentiment." if score > 0.9 else "This text expresses a **positive** sentiment."
        color_class = "sentiment-positive"
    elif label.lower() == "negative" or label.lower() == "label_0":
        emoji = "😠"
        description = "This text expresses a **highly negative** sentiment." if score > 0.9 else "This text expresses a **negative** sentiment."
        color_class = "sentiment-negative"
    elif label.lower() == "neutral" or label.lower() == "label_1":
        emoji = "😐"
        description = "This text expresses a **neutral** sentiment."
        color_class = "sentiment-neutral"
    else:
        emoji = "❓"
        description = "Could not confidently determine sentiment. Unexpected label."
        color_class = ""
    
    return f"<div class='sentiment-display {color_class}'>{emoji} {label.upper()} ({score:.2f})</div>" + \
           f"<p>{description}</p>"

# --- Sentiment Analysis Function ---
def analyze_sentiment(text):
    if not model_loaded_successfully:
        # Devuelve 3 valores: HTML, dict, string
        return ( 
            "<div class='sentiment-display'>⚠️ Model Not Loaded ⚠️</div><p>Please contact the administrator. The sentiment analysis model failed to load.</p>",
            {}, # Diccionario vacío para Confidence Scores
            "Model loading failed." # String de error para Raw Output
        )

    if not text.strip():
        # Devuelve 3 valores: HTML, dict, string
        return ( 
            "<div class='sentiment-display'>✍️ Please enter some text! ✍️</div><p>Start typing to analyze its sentiment.</p>",
            {}, # Diccionario vacío para Confidence Scores
            "" # String vacío para Raw Output
        )

    try:
        # Asegúrate de que la salida del pipeline es una lista de listas, y toma la primera.
        results = sentiment_analyzer(text)[0] 

        # Ordenar los resultados por puntuación de confianza de mayor a menor
        results_sorted = sorted(results, key=lambda x: x['score'], reverse=True)

        # Tomar el primer elemento (el de mayor confianza)
        top_sentiment = results_sorted[0] 
        label = top_sentiment['label']
        score = top_sentiment['score']

        # Crear un diccionario de puntuaciones de confianza para la salida de la etiqueta
        confidence_scores_output = {item['label']: item['score'] for item in results}

        # Generar el HTML para mostrar el sentimiento general
        overall_sentiment_display = interpret_sentiment(label, score)

        # ¡CAMBIO CLAVE AQUÍ! Ahora devuelve una tupla con 3 valores separados
        return (overall_sentiment_display, confidence_scores_output, str(results))
    except Exception as e:
        # En caso de cualquier error durante el análisis, devuelve 3 valores de error
        return ( 
            f"<div class='sentiment-display'>❌ Error ❌</div><p>An error occurred during analysis: {e}</p>",
            {}, # Diccionario vacío para Confidence Scores
            f"Error: {e}" # String de error para Raw Output
        )

# --- Gradio Interface ---
# Al establecer theme=None, Gradio no aplicará ningún tema predefinido.
# Todo el estilo visual vendrá de nuestro `custom_css`.
with gr.Blocks(css=custom_css, theme=None) as demo: 
    gr.Markdown("<h1 style='color: #80cbc4; text-align: center;'>🌌 Sentiment Analyzer 🌌</h1>")
    gr.Markdown("<p style='color: #f8f8f2; text-align: center;'>Uncover the emotional tone of your English text instantly.</p>")

    with gr.Column(elem_classes="gradio-container"):
        text_input = gr.Textbox(
            lines=7,
            placeholder="Type your English text here...",
            label="Your Text",
            interactive=True,
            value="This movie was absolutely brilliant! A masterpiece of storytelling and emotion."
        )
        analyze_btn = gr.Button("Analyze Sentiment", variant="primary")

        gr.Markdown("<hr style='border-top: 1px solid #424242;'>")
        gr.Markdown("<h3 style='color: #80cbc4; text-align: center;'>Try some examples:</h3>")
        
        # IMPORTANTE: Desactivamos cache_examples para evitar el FileNotFoundError
        examples = gr.Examples(
            examples=[
                ["This product exceeded my expectations, truly amazing!"],
                ["I found the customer service to be quite disappointing and slow."],
                ["The weather forecast predicts light rain for tomorrow morning."],
                ["What a fantastic experience! Highly recommend it."],
                ["I'm so frustrated with this slow internet connection."],
                ["The meeting concluded without any major decisions."]
            ],
            inputs=text_input,
            fn=analyze_sentiment,
            # Asegúrate de que estos 3 outputs coinciden con los 3 valores que devuelve analyze_sentiment
            outputs=[gr.HTML(label="Overall Sentiment"), gr.Label(num_top_classes=3, label="Confidence Scores"), gr.JSON(label="Raw Model Output", visible=False)],
            cache_examples=False # ESTE ES EL CAMBIO CLAVE PARA ELIMINAR EL FileNotFoundError
        )

        gr.Markdown("<hr style='border-top: 1px solid #424242;'>")
        gr.Markdown("<h2 style='color: #80cbc4;'>📊 Analysis Results</h2>")
        
        # Estas variables de salida deben coincidir en tipo y orden con lo que devuelve analyze_sentiment
        overall_sentiment_output = gr.HTML(label="Overall Sentiment")
        confidence_scores_output = gr.Label(num_top_classes=3, label="Confidence Scores")
        raw_output = gr.JSON(label="Raw Model Output", visible=False)

    # --- Event Listeners ---
    # Los outputs aquí también deben coincidir con los 3 valores que devuelve analyze_sentiment
    analyze_btn.click(
        fn=analyze_sentiment,
        inputs=text_input,
        outputs=[overall_sentiment_output, confidence_scores_output, raw_output]
    )
    text_input.change(
        fn=analyze_sentiment,
        inputs=text_input,
        outputs=[overall_sentiment_output, confidence_scores_output, raw_output],
        # live=True # Puedes descomentar si quieres actualizaciones en vivo (consume más recursos)
    )

# Launch the Gradio application
demo.launch()