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 with the NEW COLOR PALETTE ---
custom_css = """
body {
    background-color: #1E2B38; /* Fondo General Oscuro */
    color: #FFFFFF; /* Blanco para texto principal */
    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}
.gradio-container {
    box-shadow: 0 4px 8px rgba(0, 122, 204, 0.2); /* Sombra con Azul Oscuro */
    border-radius: 10px;
    overflow: hidden;
    background-color: #1E2B38; /* Fondo de la tarjeta, coincide con el body para un look unificado */
    padding: 20px;
    margin-bottom: 20px;
    border: 1px solid #007ACC; /* Borde sutil con Azul Oscuro */
}
h1, h2, h3 {
    color: #00BFFF; /* Azul Brillante para títulos */
    text-align: center;
    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
    animation: fadeIn 1s ease-in-out;
    margin-top: 5px; /* Reducir margen superior de títulos */
    margin-bottom: 15px; /* Reducir margen inferior de títulos */
}
p {
    color: #AAB7C4; /* Gris medio para texto secundario */
    text-align: center;
    margin-bottom: 20px; /* Margen debajo de los párrafos */
}
.gr-textbox label, .gradio-output .label {
    color: #AAB7C4 !important; /* Gris medio para las etiquetas de los componentes */
    font-weight: bold;
}
.gr-textbox textarea {
    background-color: #007ACC20; /* Azul Oscuro muy transparente para el fondo del textarea */
    border: 1px solid #007ACC; /* Borde con Azul Oscuro */
    color: #FFFFFF; /* Texto blanco en el textarea */
    border-radius: 6px;
    padding: 10px;
}
.gr-button.primary {
    background-color: #00BFFF !important; /* Azul Brillante para el botón primario */
    color: #1E2B38 !important; /* Texto oscuro para el botón primario */
    border-radius: 6px;
    transition: background-color 0.3s ease;
    padding: 12px 25px; /* Ajuste de padding para el botón */
    font-size: 1.1em;
    font-weight: bold;
    margin-top: 15px; /* Margen superior para separar del textarea */
}
.gr-button.primary:hover {
    background-color: #007ACC !important; /* Azul Oscuro al pasar el ratón */
    color: #FFFFFF !important; /* Texto blanco al pasar el ratón */
}
.gradio-output {
    border: 1px solid #4A5B6C; /* Borde sutil con Gris Claro */
    border-radius: 8px;
    padding: 15px;
    margin-top: 15px;
    background-color: #007ACC15; /* Fondo más sutil para la salida */
    color: #FFFFFF; /* Texto blanco en la salida */
}
.sentiment-display {
    text-align: center;
    padding: 10px;
    border-radius: 6px;
    margin-top: 10px;
    font-size: 1.2em; /* Un poco más grande para el resultado principal */
    font-weight: bold;
    color: #FFFFFF; /* Texto blanco para todos los sentimientos */
}
.sentiment-positive {
    background-color: #28a745; /* Verde Bootstrap similar */
}
.sentiment-negative {
    background-color: #dc3545; /* Rojo Bootstrap similar */
}
.sentiment-neutral {
    background-color: #007BFF; /* Azul Bootstrap similar */
}
.gradio-example-highlighted {
    background-color: #00BFFF20 !important; /* Un poco de azul brillante para ejemplos seleccionados */
    border: 1px solid #00BFFF !important;
}
.gradio-example-button {
    background-color: #4A5B6C !important; /* Gris Claro para los botones de ejemplo */
    color: #FFFFFF !important;
    border: 1px solid #4A5B6C;
    border-radius: 5px;
    padding: 8px 12px;
    margin: 5px;
    transition: background-color 0.3s ease;
}
.gradio-example-button:hover {
    background-color: #007ACC !important; /* Azul Oscuro al pasar el ratón por los ejemplos */
    border-color: #00BFFF !important;
}
hr {
    border-top: 1px solid #4A5B6C; /* Línea divisoria con Gris Claro */
    margin-top: 25px;
    margin-bottom: 25px;
}
@keyframes fadeIn {
    from { opacity: 0; }
    to { opacity: 1; }
}
"""

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

    # Asegúrate de que las etiquetas coincidan con las salidas reales de tu modelo
    # 'LABEL_0' es negativo, 'LABEL_1' es neutral, 'LABEL_2' es positivo
    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 style='color: #FFFFFF;'>{description}</p>" # Asegurar que la descripción también sea blanca

# --- Sentiment Analysis Function ---
def analyze_sentiment(text):
    if not model_loaded_successfully:
        return ( 
            "<div class='sentiment-display'>⚠️ Model Not Loaded ⚠️</div><p style='color: #FFFFFF;'>Please contact the administrator. The sentiment analysis model failed to load.</p>",
            {}, 
            {"error": "Model loading failed."} 
        )

    if not text.strip():
        return ( 
            "<div class='sentiment-display'>✍️ Please enter some text! ✍️</div><p style='color: #FFFFFF;'>Start typing to analyze its sentiment.</p>",
            {}, 
            {"info": "No text entered."} 
        )

    try:
        results = sentiment_analyzer(text)[0] 

        results_sorted = sorted(results, key=lambda x: x['score'], reverse=True)

        top_sentiment = results_sorted[0] 
        label = top_sentiment['label']
        score = top_sentiment['score']

        confidence_scores_output = {item['label']: item['score'] for item in results}

        overall_sentiment_display = interpret_sentiment(label, score)

        return (overall_sentiment_display, confidence_scores_output, results) 
        
    except Exception as e:
        return ( 
            f"<div class='sentiment-display'>❌ Error ❌</div><p style='color: #FFFFFF;'>An error occurred during analysis: {e}</p>",
            {}, 
            {"error_message": str(e)} 
        )

# --- Gradio Interface ---
with gr.Blocks(css=custom_css, theme=None) as demo: 
    gr.Markdown("<h1 style='color: #00BFFF;'>🌌 Sentiment Analyzer 🌌</h1>")
    gr.Markdown("<p style='color: #AAB7C4;'>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>")
        gr.Markdown("<h3 style='color: #00BFFF;'>Try some examples:</h3>")
        
        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,
            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 
        )

        gr.Markdown("<hr>")
        gr.Markdown("<h2 style='color: #00BFFF;'>📊 Analysis Results</h2>")
        
        overall_sentiment_output = gr.HTML(label="Overall Sentiment")
        confidence_scores_output = gr.Label(num_top_classes=3, label="Confidence Scores")
        # Deja Raw Model Output como visible=False para ahorrar espacio en el iframe por defecto
        raw_output = gr.JSON(label="Raw Model Output", visible=False) 

    # --- Event Listeners ---
    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()