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Update app.py
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app.py
CHANGED
@@ -6,6 +6,7 @@ from evaluate.utils import launch_gradio_widget
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import gradio as gr
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import torch
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import pandas as pd
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import classify
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import replace_explitives
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from whisper.model import Whisper
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@@ -115,6 +116,7 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
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# plot.update(x=classification_df["labels"], y=classification_df["scores"])
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if toxicity_score > threshold:
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print("threshold exceeded!! Launch intervention")
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return toxicity_score, classification_output, transcribed_text
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# return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
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@@ -153,8 +155,29 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
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if toxicity_score > threshold:
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print("threshold exceeded!! Launch intervention")
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return 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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explit_preference = gr.Radio(choices=["N-Word", "B-Word", "All Explitives"], label="Words to omit from general anxiety classes", info="certain words may be acceptible within certain contects for given groups of people, and some people may be unbothered by explitives broadly speaking.")
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@@ -168,7 +191,8 @@ with gr.Blocks() as iface:
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with gr.Column():
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out_val = gr.Textbox()
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out_class = gr.Textbox()
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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, explit_preference, sense_slider, intervention_type], outputs=[out_val, out_class, out_text])
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iface.launch()
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import gradio as gr
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import torch
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import pandas as pd
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import random
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import classify
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import replace_explitives
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from whisper.model import Whisper
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# plot.update(x=classification_df["labels"], y=classification_df["scores"])
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if toxicity_score > threshold:
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print("threshold exceeded!! Launch intervention")
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intervention_output(intervention)
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return toxicity_score, classification_output, transcribed_text
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# return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
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if toxicity_score > threshold:
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print("threshold exceeded!! Launch intervention")
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return classify_anxiety
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def intervention_output(intervene):
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if intervene== "Audio File":
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gr.Audio.play("calm.wav")
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elif intervene == "Therapy App":
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out_img : gr.update(visible=True, value="hrv-breathing.gif")
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elif intervene == "Text Message":
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phrase = positive_affirmations()
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out_text : gr.update(visible=True, value=phrase)
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else:
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pass
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def positive_affirmations():
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affirmations = [
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"I have survived my anxiety before and I will survive again now",
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"I am not in danger; I am just uncomfortable; this too will pass",
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"I forgive and release the past and look forward to the future",
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"I can't control what other people say but I can control my breathing and my response"
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]
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selected_affirm = random.choice(affirmations)
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return selected_affirm
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with gr.Blocks() as iface:
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intervene_State = gr.State([])
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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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explit_preference = gr.Radio(choices=["N-Word", "B-Word", "All Explitives"], label="Words to omit from general anxiety classes", info="certain words may be acceptible within certain contects for given groups of people, and some people may be unbothered by explitives broadly speaking.")
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with gr.Column():
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out_val = gr.Textbox()
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out_class = gr.Textbox()
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out_text = gr.Textbox(visible=False))
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out_img = gr.Textbox(visible=False))
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submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, anxiety_class, emo_class, explit_preference, sense_slider, intervention_type], outputs=[out_val, out_class, out_text])
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iface.launch()
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