Update app.py

app.py

@@ -5,7 +5,7 @@ from functools import partial
 
 import requests
 import pandas as pd
-import plotly.express as px
+#import plotly.express as px
 
 import torch
 import gradio as gr
@@ -21,41 +21,35 @@ device = 0 if torch.cuda.is_available() else -1
 
 # display if the sentiment value is above these thresholds
 thresholds = {"joy": 0.99,"anger": 0.95,"surprise": 0.95,"sadness": 0.98,"fear": 0.95,"love": 0.99,}
-
 color_map = {"joy": "green","anger": "red","surprise": "yellow","sadness": "blue","fear": "orange","love": "purple",}
 
 # Audio components
 whisper_device = "cuda" if torch.cuda.is_available() else "cpu"
 whisper = whisperx.load_model("tiny.en", whisper_device)
 alignment_model, metadata = whisperx.load_align_model(language_code="en", device=whisper_device)
-speaker_segmentation = Pipeline.from_pretrained("pyannote/[email protected]",
-                                    use_auth_token=os.environ['ENO_TOKEN'])
+speaker_segmentation = Pipeline.from_pretrained("pyannote/[email protected]", use_auth_token=os.environ['ENO_TOKEN'])
 
+speech_to_text = partial(
+    stt, 
+    speaker_segmentation=speaker_segmentation, 
+    whisper=whisper, 
+    alignment_model=alignment_model, 
+    metadata=metadata, 
+    whisper_device=whisper_device
+    )
 
 # Text components
 emotion_pipeline = pipeline(
     "text-classification",
     model="bhadresh-savani/distilbert-base-uncased-emotion",
-    device=device,
+    #device=device,
 )
 summarization_pipeline = pipeline(
     "summarization",
     model="knkarthick/MEETING_SUMMARY",
-    device=device
+    #device=device
 )
 
-EXAMPLES = [["Customer_Support_Call.wav"]]
-
-
-speech_to_text = partial(
-    stt, 
-    speaker_segmentation=speaker_segmentation, 
-    whisper=whisper, 
-    alignment_model=alignment_model, 
-    metadata=metadata, 
-    whisper_device=whisper_device
-    )
-
 def summarize(diarized, summarization_pipeline):
     text = ""
     for d in diarized:
@@ -78,6 +72,8 @@ def sentiment(diarized, emotion_pipeline):
 
     return customer_sentiments
 
+EXAMPLES = [["Customer_Support_Call.wav"]]
+
 with gr.Blocks() as demo:
 
     with gr.Row():