mskov commited on
Commit
b31f1e8
1 Parent(s): b9a0cdb

Update app.py

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Files changed (1) hide show
  1. app.py +7 -3
app.py CHANGED
@@ -44,7 +44,8 @@ def classify_emotion():
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  #### Emotion classification ####
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  emotion_classifier = foreign_class(source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")
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  out_prob, score, index, text_lab = emotion_classifier.classify_file(audio_file)
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- return emo_dict[text_lab[0]]
 
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  # Create a Gradio interface with audio file and text inputs
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  def classify_toxicity(audio_file, text_input, classify_anxiety):
@@ -64,7 +65,9 @@ def classify_toxicity(audio_file, text_input, classify_anxiety):
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  toxicity_score = toxicity_results["toxicity"][0]
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  print(toxicity_score)
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-
 
 
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  #### Text classification #####
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  device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
@@ -118,12 +121,13 @@ def classify_toxicity(audio_file, text_input, classify_anxiety):
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  with gr.Blocks() as iface:
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  with gr.Column():
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  anxiety_class = gr.Radio(["racism", "LGBTQ+ hate", "sexually explicit", "misophonia"])
 
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  with gr.Column():
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  aud_input = gr.Audio(source="upload", type="filepath", label="Upload Audio File")
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  text = gr.Textbox(label="Enter Text", placeholder="Enter text here...")
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  submit_btn = gr.Button(label="Run")
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  with gr.Column():
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  out_text = gr.Textbox()
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- submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, anxiety_class], outputs=out_text)
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  iface.launch()
 
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  #### Emotion classification ####
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  emotion_classifier = foreign_class(source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")
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  out_prob, score, index, text_lab = emotion_classifier.classify_file(audio_file)
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+ emostr = "emo class"
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+ return emo_dict[text_lab[0]], emostr
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  # Create a Gradio interface with audio file and text inputs
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  def classify_toxicity(audio_file, text_input, classify_anxiety):
 
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  toxicity_score = toxicity_results["toxicity"][0]
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  print(toxicity_score)
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+ # emo call
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+ if emo_class != None:
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+ classify_emotion()
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  #### Text classification #####
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  device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
 
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  with gr.Blocks() as iface:
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  with gr.Column():
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  anxiety_class = gr.Radio(["racism", "LGBTQ+ hate", "sexually explicit", "misophonia"])
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+ emo_class = gr.Radio(choices=["negaitve emotionality"], label="label", info="info")
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  with gr.Column():
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  aud_input = gr.Audio(source="upload", type="filepath", label="Upload Audio File")
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  text = gr.Textbox(label="Enter Text", placeholder="Enter text here...")
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  submit_btn = gr.Button(label="Run")
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  with gr.Column():
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  out_text = gr.Textbox()
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+ submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, anxiety_class, emo_class], outputs=out_text)
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  iface.launch()