Update app.py

app.py

@@ -5,7 +5,6 @@ from functools import partial
 
 import requests
 import pandas as pd
-#import plotly.express as px
 
 import torch
 import gradio as gr
@@ -20,7 +19,7 @@ os.environ["TOKENIZERS_PARALLELISM"] = "false"
 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,}
+#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
@@ -42,19 +41,16 @@ speech_to_text = partial(
 emotion_pipeline = pipeline(
     "text-classification",
     model="bhadresh-savani/distilbert-base-uncased-emotion",
-    #device=device,
 )
 summarization_pipeline = pipeline(
     "summarization",
     model="knkarthick/MEETING_SUMMARY",
-    #device=device
 )
 
 def summarize(diarized, summarization_pipeline):
     text = ""
     for d in diarized:
         text += f"\n{d[1]}: {d[0]}"
-
     return summarization_pipeline(text)[0]["summary_text"]
 
 def sentiment(diarized, emotion_pipeline):
@@ -67,8 +63,8 @@ def sentiment(diarized, emotion_pipeline):
         if "Customer" in speaker_id:
             outputs = emotion_pipeline(sentences)
             for idx, (o, t) in enumerate(zip(outputs, sentences)):
-                if o["score"] > thresholds[o["label"]]:
-                    customer_sentiments.append((t, o["label"]))
+  #              if o["score"] > thresholds[o["label"]]:
+                 customer_sentiments.append((t, o["label"]))
 
     return customer_sentiments