Update app.py

app.py

@@ -1,47 +1,203 @@
 import gradio as gr
 from transformers import pipeline
 
-# ID de tu modelo de análisis de sentimientos que ya subiste
+# --- Model Loading ---
+# Using the model ID you've been working with, which is fine-tuned on an English dataset (IMDB).
+# It's crucial that this model is indeed fine-tuned for sentiment analysis in English.
 MODEL_ID = "Light-Dav/sentiment-analysis-full-project"
 
-# Cargar el pipeline del modelo
 try:
-    classifier = pipeline("sentiment-analysis", model=MODEL_ID)
+    # Attempt to load the pipeline. This needs to be outside the function for efficiency.
+    # The 'return_all_scores=True' is important for getting scores for all labels.
+    sentiment_analyzer = pipeline("sentiment-analysis", model=MODEL_ID, return_all_scores=True)
+    model_loaded_successfully = True
 except Exception as e:
-    print(f"Error al cargar el modelo {MODEL_ID}: {e}")
-    classifier = None # Para evitar errores si classifier no se inicializa
+    print(f"Error loading model: {e}")
+    sentiment_analyzer = None
+    model_loaded_successfully = False
 
+# --- Custom CSS for a unique look ---
+custom_css = """
+body {
+    background-color: #f0f2f5; /* Light grey background */
+}
+.gradio-container {
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    border-radius: 15px;
+    overflow: hidden;
+    background-color: #ffffff; /* White card background */
+}
+h1, h2, h3 {
+    color: #4CAF50; /* Green accents */
+    text-align: center;
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    animation: fadeIn 1s ease-in-out;
+}
+.gr-button.primary {
+    background-color: #4CAF50 !important;
+    color: white !important;
+    border-radius: 8px;
+    transition: background-color 0.3s ease;
+}
+.gr-button.primary:hover {
+    background-color: #45a049 !important;
+}
+.gradio-output {
+    border: 1px solid #e0e0e0;
+    border-radius: 10px;
+    padding: 15px;
+    margin-top: 20px;
+    background-color: #f9f9f9;
+}
+.sentiment-display {
+    text-align: center;
+    padding: 10px;
+    border-radius: 8px;
+    margin-top: 15px;
+    font-size: 1.2em;
+    font-weight: bold;
+}
+.sentiment-positive {
+    background-color: #e6ffe6; /* Light green */
+    color: #28a745; /* Darker green */
+}
+.sentiment-negative {
+    background-color: #ffe6e6; /* Light red */
+    color: #dc3545; /* Darker red */
+}
+.sentiment-neutral {
+    background-color: #e6f7ff; /* Light blue */
+    color: #007bff; /* Darker blue */
+}
+@keyframes fadeIn {
+    from { opacity: 0; }
+    to { opacity: 1; }
+}
+"""
+
+# --- Helper Function for Sentiment Interpretation ---
+def interpret_sentiment(label, score):
+    emoji = ""
+    description = ""
+    color_class = ""
+
+    if label.lower() == "positive": # Your model might output 'LABEL_2' or 'POS'
+        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": # Your model might output 'LABEL_0' or 'NEG'
+        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": # Your model might output 'LABEL_1' or 'NEUTRAL'
+        emoji = "😐"
+        description = "This text expresses a **neutral** sentiment."
+        color_class = "sentiment-neutral"
+    else:
+        emoji = "❓"
+        description = "Could not confidently determine sentiment."
+        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 text:
-        return {"Positive": 0.0, "Negative": 0.0, "Neutral": 0.0}
-
-    if classifier is None:
-        return {"Error": "Modelo no cargado. Contactar al administrador."}
-
-    # Realizar la inferencia
-    # pipeline devuelve una lista de diccionarios, tomamos el primero
-    results = classifier(text)[0]
-
-    # Formatear el resultado para Gradio (diccionario de etiqueta a score)
-    formatted_results = {}
-    for item in results:
-        formatted_results[item['label']] = item['score']
-
-    return formatted_results
-
-# Crear la interfaz de Gradio
-iface = gr.Interface(
-    fn=analyze_sentiment,
-    inputs=gr.Textbox(lines=3, placeholder="Escribe tu texto aquí..."),
-    outputs=gr.Label(num_top_classes=3), # Mostrar las 3 clases principales (Positivo, Negativo, Neutro)
-    title="🤖 Análisis de Sentimientos en Español",
-    description="Introduce un texto en español y el modelo predecirá su sentimiento (Positivo, Negativo, Neutro).",
-    examples=[
-        ["Me encantó este libro, es fascinante y lo recomiendo totalmente."],
-        ["El servicio fue terrible, muy lento y poco amable."],
-        ["La reunión se programó para el jueves a las 10 AM."]
-    ]
-)
-
-# Iniciar la aplicación Gradio (esto se hace automáticamente en Hugging Face Spaces)
-iface.launch(share=False)
+    if not model_loaded_successfully:
+        return {
+            "Overall Sentiment": "<div class='sentiment-display'>⚠️ Model Not Loaded ⚠️</div><p>Please contact the administrator. The sentiment analysis model failed to load.</p>",
+            "Confidence Scores": {},
+            "Raw Output": "Model loading failed."
+        }
+
+    if not text.strip():
+        return {
+            "Overall Sentiment": "<div class='sentiment-display'>✍️ Please enter some text! ✍️</div><p>Start typing to analyze its sentiment.</p>",
+            "Confidence Scores": {},
+            "Raw Output": ""
+        }
+
+    try:
+        # The pipeline returns a list of dictionaries if return_all_scores=True
+        # e.g., [[{'label': 'LABEL_0', 'score': 0.9}, {'label': 'LABEL_1', 'score': 0.05}]]
+        results = sentiment_analyzer(text)[0] # Get the first (and only) list of results
+
+        # Sort results by score in descending order
+        results_sorted = sorted(results, key=lambda x: x['score'], reverse=True)
+
+        # Get the top sentiment
+        top_sentiment = results_sorted[0]
+        label = top_sentiment['label']
+        score = top_sentiment['score']
+
+        # Format for Gradio Label component (Dictionary {label: score})
+        # This is for the 'Confidence Scores' output
+        confidence_scores_output = {item['label']: item['score'] for item in results}
+
+        # Interpret sentiment for the 'Overall Sentiment' output
+        overall_sentiment_display = interpret_sentiment(label, score)
+
+        return {
+            "Overall Sentiment": overall_sentiment_display,
+            "Confidence Scores": confidence_scores_output,
+            "Raw Output": str(results)
+        }
+    except Exception as e:
+        return {
+            "Overall Sentiment": f"<div class='sentiment-display'>❌ Error ❌</div><p>An error occurred during analysis: {e}</p>",
+            "Confidence Scores": {},
+            "Raw Output": f"Error: {e}"
+        }
+
+# --- Gradio Interface ---
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo: # Using gr.Blocks for more layout control
+    gr.Markdown("# ✨ Sentiment Spark ✨")
+    gr.Markdown("---")
+    gr.Markdown("### Uncover the emotional tone of your English text instantly!")
+
+    with gr.Row(): # Horizontal layout for input and outputs
+        with gr.Column(scale=2):
+            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") # The main action button
+
+            gr.Markdown("---")
+            gr.Markdown("### Try some 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,
+                cache_examples=True # Caching examples for faster loading
+            )
+
+        with gr.Column(scale=3):
+            gr.Markdown("## 📈 Analysis Results 📉")
+            overall_sentiment_output = gr.HTML(label="Overall Sentiment") # Using HTML to render custom styled sentiment
+            confidence_scores_output = gr.Label(num_top_classes=3, label="Confidence Scores") # Default Gradio Label
+            raw_output = gr.JSON(label="Raw Model Output", visible=False) # For debugging/advanced users
+
+    # --- Event Listeners ---
+    text_input.change(
+        fn=analyze_sentiment,
+        inputs=text_input,
+        outputs=[overall_sentiment_output, confidence_scores_output, raw_output],
+        # live=True # Uncomment for live updates as user types (can be resource intensive)
+    )
+    analyze_btn.click(
+        fn=analyze_sentiment,
+        inputs=text_input,
+        outputs=[overall_sentiment_output, confidence_scores_output, raw_output]
+    )
+
+demo.launch()