Cambio a slider

app.py

@@ -2,41 +2,65 @@ import gradio as gr
 from transformers import pipeline
 from PyPDF2 import PdfReader
 
-# Inicializar el modelo de análisis de sentimiento con DistilBETO
-sentiment_analysis = pipeline("sentiment-analysis", model="finiteautomata/beto-sentiment-analysis")
-
-def analyze_text(text):
-    result = sentiment_analysis(text)
-    label = result[0]['label']
-    score = result[0]['score']
-    return f"Label: {label}, Score: {score}"
-
-def analyze_pdf(pdf):
-    # Leer el contenido del PDF
-    pdf_reader = PdfReader(pdf)
+# Cargar el modelo de DistilBETO
+sentiment_analysis = pipeline("sentiment-analysis", 
+                              model="nlptown/bert-base-multilingual-uncased-sentiment")
+
+# Función para analizar texto
+def analyze_text_sentiment(text):
+    result = sentiment_analysis(text)[0]
+    score = result['score']
+    label = result['label']
+    
+    # Convertir la etiqueta de sentimiento en un valor para el slider y su literal
+    if label == "1 star":
+        slider_value = 0
+        literal = "Muy negativo"
+    elif label == "2 stars":
+        slider_value = 25
+        literal = "Negativo"
+    elif label == "3 stars":
+        slider_value = 50
+        literal = "Neutro"
+    elif label == "4 stars":
+        slider_value = 75
+        literal = "Positivo"
+    elif label == "5 stars":
+        slider_value = 100
+        literal = "Muy positivo"
+
+    # Añadir el nivel de confianza al literal
+    confidence = round(score * 100, 2)
+    literal_with_confidence = f"{literal} (Confianza: {confidence}%)"
+
+    return slider_value, literal_with_confidence
+
+# Función para extraer texto de PDF y analizar
+def analyze_pdf_sentiment(pdf_file):
+    pdf_reader = PdfReader(pdf_file.name)
     text = ""
     for page in pdf_reader.pages:
-        text += page.extract_text() + "\n"
+        text += page.extract_text()
     
-    # Análisis del contenido del PDF
-    result = sentiment_analysis(text)
-    label = result[0]['label']
-    score = result[0]['score']
-    return f"Label: {label}, Score: {score}"
+    return analyze_text_sentiment(text)
 
-# Interfaz de Gradio con dos inputs: texto o PDF
+# Interfaz de Gradio
 with gr.Blocks() as demo:
-    gr.Markdown("# Análisis de Sentimientos con DistilBETO")
-    gr.Markdown("Ingrese un texto en español o cargue un PDF para analizar su sentimiento usando DistilBETO.")
-    
-    with gr.Row():
-        text_input = gr.Textbox(label="Texto", placeholder="Escribe aquí el texto para análisis")
-        pdf_input = gr.File(label="Cargar PDF", type="file")
-    
-    output = gr.Textbox(label="Resultado")
-    
-    submit_btn = gr.Button("Submit")
-    submit_btn.click(fn=analyze_text, inputs=text_input, outputs=output)
-    submit_btn.click(fn=analyze_pdf, inputs=pdf_input, outputs=output)
+    with gr.Tabs():
+        with gr.Tab("Texto"):
+            text_input = gr.Textbox(label="Ingrese su texto aquí", lines=5)
+            analyze_button = gr.Button("Analizar")
+            slider_output = gr.Slider(label="Nivel de Sentimiento", minimum=0, maximum=100, step=1, interactive=False)
+            label_output = gr.Label()
+
+            analyze_button.click(analyze_text_sentiment, inputs=text_input, outputs=[slider_output, label_output])
+
+        with gr.Tab("PDF"):
+            pdf_input = gr.File(label="Subir PDF", file_types=[".pdf"])
+            analyze_button = gr.Button("Analizar")
+            slider_output = gr.Slider(label="Nivel de Sentimiento", minimum=0, maximum=100, step=1, interactive=False)
+            label_output = gr.Label()
+
+            analyze_button.click(analyze_pdf_sentiment, inputs=pdf_input, outputs=[slider_output, label_output])
 
 demo.launch()