feat: sentiment analysis and blocks

app.py

@@ -1,15 +1,35 @@
-from transformers import pipeline
 import gradio as gr
+from transformers import pipeline
 
+trans = pipeline("automatic-speech-recognition", "facebook/wav2vec2-large-xlsr-53-spanish")
+clasificador = pipeline("text-classification", model = "pysentimiento/robertuito-sentiment-analysis")
 
-modelo = pipeline("automatic-speech-recognition", model = "facebook/wav2vec2-large-xlsr-53-spanish")
-
-def transcribe(audio):
-  text = modelo(audio)["text"]
+def audio_a_texto(audio):
+  text = trans(audio)["text"]
   return text
 
-gr.Interface(
-    fn = transcribe,
-    inputs = [ gr.Audio(source="microphone", type="filepath") ],
-    outputs = ["textbox"]
-).launch()
+def texto_a_sentimiento(text):
+  return clasificador(text)[0]["label"]
+
+demo = gr.Blocks()
+
+with demo:
+  gr.Markdown("Demo")
+  audio = gr.Audio(source = "microphone", type = "filepath")
+  texto = gr.Textbox()
+  b1 = gr.Button("Transcribe!")
+  b1.click(
+    fn = audio_a_texto,
+    inputs = audio,
+    outputs = texto
+  )
+
+  label = gr.Label()
+  b2 = gr.Button("Clasify!")
+  b2.click(
+    fn = texto_a_sentimiento,
+    inputs = texto,
+    outputs = label
+  )
+
+demo.launch()