Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 from transformers import pipeline
 import os
+import random # ¡Importamos random para la selección aleatoria!
 
 # Añade esto para verificar la versión de Gradio en tiempo de ejecución
 print(f"Gradio version at runtime: {gr.__version__}")
@@ -86,7 +87,7 @@ p {
 
 /* BOTONES PRINCIPALES */
 .gr-button.primary {
-    background-color: #00BFFF !important; /* Azul Brillante para el botón primario */
+    background-color: #00BFFF !important; /* ¡CAMBIO AQUÍ! Azul Brillante para el botón primario (Acento) */
     color: #1E2B38 !important; /* Texto oscuro para el botón primario */
     border-radius: 5px;
     transition: background-color 0.3s ease;
@@ -204,40 +205,38 @@ def interpret_sentiment(label, score):
     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 = "Positive sentiment detected." # Mensaje más corto
+        description = "Positive sentiment detected."
         color_class = "sentiment-positive"
     elif label.lower() == "negative" or label.lower() == "label_0":
         emoji = "😠"
-        description = "Negative sentiment detected." # Mensaje más corto
+        description = "Negative sentiment detected."
         color_class = "sentiment-negative"
     elif label.lower() == "neutral" or label.lower() == "label_1":
         emoji = "😐"
-        description = "Neutral sentiment detected." # Mensaje más corto
+        description = "Neutral sentiment detected."
         color_class = "sentiment-neutral"
     else:
         emoji = "❓"
-        description = "Sentiment not determined." # Mensaje más corto
+        description = "Sentiment not determined."
         color_class = ""
     
     return f"<div class='sentiment-display {color_class}'>{emoji} {label.upper()} ({score:.2f})</div>" + \
-           f"<p style='color: #FFFFFF; font-size: 0.75em; margin-top: 3px; margin-bottom: 0;'>{description}</p>" # Asegurar que la descripción sea pequeña
+           f"<p style='color: #FFFFFF; font-size: 0.75em; margin-top: 3px; margin-bottom: 0;'>{description}</p>"
 
 # --- Sentiment Analysis Function ---
 def analyze_sentiment(text):
     if not model_loaded_successfully:
         return ( 
-            "<div class='sentiment-display'>⚠️ Model Error ⚠️</div><p style='color: #FFFFFF; font-size: 0.75em;'>Model not loaded.</p>", # Mensaje más corto
+            "<div class='sentiment-display'>⚠️ Model Error ⚠️</div><p style='color: #FFFFFF; font-size: 0.75em;'>Model not loaded.</p>",
             {}, 
             {"error": "Model loading failed."} 
         )
 
     if not text.strip():
         return ( 
-            "<div class='sentiment-display'>✍️ Enter text ✍️</div><p style='color: #FFFFFF; font-size: 0.75em;'>Type text to analyze.</p>", # Mensaje más corto
+            "<div class='sentiment-display'>✍️ Enter text ✍️</div><p style='color: #FFFFFF; font-size: 0.75em;'>Type text to analyze.</p>",
             {}, 
             {"info": "No text entered."} 
         )
@@ -259,15 +258,35 @@ def analyze_sentiment(text):
         
     except Exception as e:
         return ( 
-            f"<div class='sentiment-display'>❌ Error ❌</div><p style='color: #FFFFFF; font-size: 0.75em;'>Analysis failed.</p>", # Mensaje más corto
+            f"<div class='sentiment-display'>❌ Error ❌</div><p style='color: #FFFFFF; font-size: 0.75em;'>Analysis failed.</p>",
             {}, 
             {"error_message": str(e)} 
         )
 
+# --- Lista completa de ejemplos para selección aleatoria ---
+ALL_EXAMPLES = [
+    ["The product quality is absolutely outstanding and worth every penny!"],
+    ["I found the customer support unhelpful and quite rude, a terrible experience."],
+    ["The new software update introduced several bugs, making it very unstable."],
+    ["This book is a captivating read, I couldn't put it down!"],
+    ["The delivery was late, and the package arrived damaged."],
+    ["Despite the bad reviews, I thoroughly enjoyed the film and its unique plot."],
+    ["The instructions were unclear, leading to a lot of confusion during assembly."],
+    ["What a delicious meal! Every dish was prepared to perfection."],
+    ["I'm very disappointed with the recent policy changes; they are unfair."],
+    ["The new art exhibition is thought-provoking and visually stunning."],
+    ["Traffic was unexpectedly heavy, causing significant delays."],
+    ["Overall, a solid performance, though there's room for improvement."]
+]
+
+# --- Función para obtener ejemplos aleatorios ---
+def get_random_examples():
+    return random.sample(ALL_EXAMPLES, 3) # Selecciona 3 ejemplos al azar
+
 # --- 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;'>Analyze the emotional tone of your English text.</p>") # Párrafo más corto
+    gr.Markdown("<p style='color: #AAB7C4;'>Analyze the emotional tone of your English text.</p>")
 
     with gr.Column(elem_classes="gradio-container"):
         text_input = gr.Textbox(
@@ -275,32 +294,27 @@ with gr.Blocks(css=custom_css, theme=None) as demo:
             placeholder="Type your English text here...",
             label="Your Text",
             interactive=True,
-            value="This movie was absolutely brilliant! A masterpiece of storytelling and emotion."
+            # Establece un ejemplo aleatorio inicial al cargar la interfaz
+            value=random.choice(ALL_EXAMPLES)[0] 
         )
         analyze_btn = gr.Button("Analyze Sentiment", variant="primary")
 
         gr.Markdown("<hr>")
-        gr.Markdown("<h3 style='color: #00BFFF;'>Try examples:</h3>") # Título de ejemplos más corto
+        gr.Markdown("<h3 style='color: #00BFFF;'>Try examples:</h3>")
         
         with gr.Row():
+            # gr.Examples ahora usa la función get_random_examples para cargar los ejemplos
+            # y no tiene el argumento elem_classes para evitar el TypeError.
             examples = gr.Examples(
-                examples=[
-                    ["Amazing product!"], # Ejemplos más cortos
-                    ["Very slow service."],
-                    ["Light rain tomorrow."],
-                    ["Fantastic experience!"],
-                    ["Frustrated with internet."],
-                    ["Meeting concluded."]
-                ],
+                examples=get_random_examples(), # Carga ejemplos aleatorios
                 inputs=text_input,
                 fn=analyze_sentiment,
-                outputs=[gr.HTML(label="Sentiment"), gr.Label(num_top_classes=3, label="Scores"), gr.JSON(label="Raw Output", visible=False)], # Labels de salida más cortos
+                outputs=[gr.HTML(label="Sentiment"), gr.Label(num_top_classes=3, label="Scores"), gr.JSON(label="Raw Output", visible=False)],
                 cache_examples=False,
-                # ELIMINADO: elem_classes="example-buttons-row"
             )
 
         gr.Markdown("<hr>")
-        gr.Markdown("<h2 style='color: #00BFFF;'>📊 Results</h2>") # Título de resultados más corto
+        gr.Markdown("<h2 style='color: #00BFFF;'>📊 Results</h2>")
         
         overall_sentiment_output = gr.HTML(label="Overall Sentiment")
         confidence_scores_output = gr.Label(num_top_classes=3, label="Confidence Scores")