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app.py

@@ -201,9 +201,9 @@ class SentimentAnalyser(object):
     utility._ph()
     print(utility.fetch_system_info())
     utility._ph()
-    print(utility.fetch_gpu_info())
+    # print(utility.fetch_gpu_info())
     utility._ph()
-    print(utility.fetch_host_ip())
+    # print(utility.fetch_host_ip())
     utility._ph()
     self._init_model()
     utility._login_hface()
@@ -211,7 +211,7 @@ class SentimentAnalyser(object):
 
   # initalise the model
   def _init_model(self):
-    modelLink = "bhadresh-savani/distilbert-base-uncased-emotion"
+    modelLink = "bhadresh-savani/distilbert-base-uncased-emotion" #"SamLowe/roberta-base-go_emotions"
     self.tokenizer = AutoTokenizer.from_pretrained(modelLink)
     self.model = AutoModelForSequenceClassification.from_pretrained(modelLink)
     self.whisper_model = whisper.load_model("small")
@@ -260,7 +260,7 @@ def inference(audio):
   return result.text
   
 @add_method(SentimentAnalyser)
-def predict_sentiment(input_text, input_audio = None, audio_text_btn = None):
+def predict_sentiment(input_text):
   df_out = _predict_sentiment(input_text)
   max_column = df_out.loc[0].idxmax()
   max_value = df_out.loc[0].max()
@@ -276,7 +276,7 @@ whisper_audio = gradio.Audio(label="Audio Input",
 whisper_button = gradio.Button("Convert Audio to Text")
 input_text = gradio.Textbox(lines=1, label="Text Input", placeholder="type text here")
 
-in_box = [input_text,whisper_audio,whisper_button]
+in_box = [input_text]
 out_box = [gradio.Plot(label="Sentiment Score:"),
   gradio.Textbox(lines=4, label="Raw JSON Response:")]