Update app.py

app.py

@@ -1,8 +1,8 @@
+
 import os
 import numpy as np
 import gradio as gr
 from gradio.mix import Series
-from transformers import pipeline
 
 path_to_L_model = str(os.environ['path_to_L_model'])
 read_token      = str(os.environ['read_token'])
@@ -10,46 +10,17 @@ read_token      = str(os.environ['read_token'])
 description = "Talk to Breud!"
 title       = "Breud (BERT + Freud)"
 
-# wisper = gr.Interface.load("models/openai/whisper-base")
-
-# interface_model_L = gr.Interface.load(
-#             name=path_to_L_model,
-#             api_key=read_token,
-# )
-
-# Series( 
-#     wisper, 
-#     interface_model_L,
-#     description = description,
-#     title = title,
-#     inputs = gr.Audio(source="microphone"),
-# ).launch()
-
-
-asr = pipeline("automatic-speech-recognition", "openai/whisper-base")
-classifier = pipeline("text-classification", path_to_L_model, api_token=read_token)
-
-
-def speech_to_text(speech):
-    text = asr(speech)["text"]
-    return text
-
-
-def text_to_sentiment(text):
-    return classifier(text)[0]["label"]
-
-
-demo = gr.Blocks()
-
-with demo:
-    audio_file = gr.Audio(source="microphone")
-    text = gr.Textbox()
-    label = gr.Label()
-
-    b1 = gr.Button("Recognize Speech")
-    b2 = gr.Button("Classify Sentiment")
+wisper = gr.Interface.load("models/openai/whisper-base")
 
-    b1.click(speech_to_text, inputs=audio_file, outputs=text)
-    b2.click(text_to_sentiment, inputs=text, outputs=label)
+interface_model_L = gr.Interface.load(
+            name=path_to_L_model,
+            api_key=read_token,
+)
 
-demo.launch()
+Series( 
+    wisper, 
+    interface_model_L,
+    description = description,
+    title = title,
+    inputs = gr.Audio(source="microphone"),
+).launch()