create app.py

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+from transformers import pipeline
+
+trans = pipeline("automatic-speech-recognition", model = "facebook/wav2vec2-large-xlsr-53-spanish")
+classificador = pipeline("text-classification", model = "pysentimiento/robertuito-sentiment-analysis")
+
+def audio_a_texto(audio):
+  text = trans(audio)['text']
+  return text
+
+
+def texto_a_sentimiento(text):
+  return classificador(text)[0]["label"]
+
+
+
+demo = gr.Blocks()
+
+with demo:
+  gr.Markdown("Second demo with Blocks")
+  with gr.Tabs():
+    with gr.TabItem("Transcribe audio"):
+      with gr.Row():
+        audio = gr.Audio(source="microphone", type = "filepath")
+        transcription = gr.Textbox()
+      b1 = gr.Button("Transcribe Audio")
+      #b1.click(fn=)
+    with gr.TabItem("Sentiment Analysis"):
+      with gr.Row():
+        text = gr.Textbox()
+        label = gr.Label()
+      b2 = gr.Button('Classify')
+    
+    b1.click(audio_a_texto, inputs = audio, outputs = transcription)
+    b2.click(texto_a_sentimiento, inputs = text, outputs = label)
+
+demo.launch()