Update app.py

app.py

@@ -3,51 +3,50 @@ import tensorflow as tf
 from transformers import pipeline
 
 inception_net = tf.keras.applications.MobileNetV2()
-def classify_imagen(inp):
+
+def classify_image(inp):
   inp = inp.reshape((-1, 224, 224, 3))
   inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
   prediction = inception_net.predict(inp).reshape(1,1000)
   pred_scores = tf.keras.applications.mobilenet_v2.decode_predictions(prediction, top=100)
-  confidence = {f'{pred_scores[0][i][1]}': float(pred_scores[0][i][2]) for i in range(100)}  
-  return confidence
+  confidences = {f'{pred_scores[0][i][1]}': float(pred_scores[0][i][2]) for i in range(100)}
+  return confidences
 
 trans = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish")
-def audio2text(audio):
+def audio_to_text(audio):
   text = trans(audio)["text"]
   return text
-    
-classificator = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis")
-def text2sentiment(text):
-  return classificator(text)[0]['label']
 
+classify = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis")
+def text_to_sentiment(text):
+  return classify(text)[0]["label"]
 
 demo = gr.Blocks()
 
 with demo:
-  gr.Markdown("Este es el segundo demo con Blocks hecho por Rafa")
+  gr.Markdown("Second Demo with Blocks")
   with gr.Tabs():
-
-    with gr.TabItem("Transcribe Audio en español"):
+    with gr.TabItem("Transcript audio in spanish"):
       with gr.Row():
-        audio = gr.Audio(source='microphone', type='filepath')
-        transcript = gr.Textbox()
-      b1 = gr.Button("Transcribe")
+        audio = gr.Audio(source="microphone", type="filepath")
+        transcription = gr.Textbox()
+      button1 = gr.Button("Please transcript")
 
-    with gr.TabItem("Analisis de sentimientos"):
+    with gr.TabItem("Sentiment analisys"):
       with gr.Row():
-        texto = gr.Textbox()
+        text = gr.Textbox()
         label = gr.Label()
-      b2 = gr.Button("Sentimientos")
-
-    b1.click(audio2text, inputs=audio, outputs=transcript)
-    b2.click(text2sentiment, inputs=texto, outputs=label)
+      button2 = gr.Button("Please sentiment")
+    
+    button1.click(audio_to_text, inputs=audio, outputs=transcription)
+    button2.click(text_to_sentiment, inputs=text, outputs=label)
 
-    with gr.TabItem("Clasificador de imagenes"):
+    with gr.TabItem("Image classify"):
       with gr.Row():
-        image = gr.Image(shape=(224, 224))
-        label= gr.Label(num_top_classes=3)
-      bimage= gr.Button("Clasificar")
-
-    bimage.click(classify_imagen, inputs=image, outputs=label)
+        image = gr.Image(shape=(224,224))
+        labelImage = gr.Label(num_top_classes=3)
+      button3 = gr.Button("Please classify Image")
+    
+    button3.click(classify_image, inputs=image, outputs=labelImage)
 
 demo.launch()