DreamStream-1 commited on
Commit
d2863d2
·
verified ·
1 Parent(s): 500e0dd

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +6 -9
app.py CHANGED
@@ -356,7 +356,6 @@ with gr.Blocks(css=custom_css) as app:
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  submit_chatbot = gr.Button(value="Submit Your Message", variant="primary", elem_id="submit-chatbot", icon="fa-paper-plane")
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- # Adding the loading prop for the chatbot button
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  chatbot = gr.Chatbot(label="Chat History", elem_id="chat-history", show_label=True)
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  sentiment = gr.Textbox(label="Detected Sentiment")
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  emotion = gr.Textbox(label="Detected Emotion")
@@ -365,13 +364,13 @@ with gr.Blocks(css=custom_css) as app:
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  professionals = gr.DataFrame(label="Nearby Health Professionals", headers=["Name", "Address"])
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  map_html = gr.HTML(label="Interactive Map")
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  submit_chatbot.click(
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  app_function_chatbot,
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  inputs=[user_input, location, query, chatbot],
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- outputs=[chatbot, sentiment, emotion, suggestions_markdown, professionals, map_html],
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- loading=True # Enable loading spinner on button click
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- )
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-
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  with gr.Tab("Disease Prediction"):
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  symptom1 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 1")
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  symptom2 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 2")
@@ -381,16 +380,14 @@ with gr.Blocks(css=custom_css) as app:
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  submit_disease = gr.Button(value="Predict Disease", variant="primary", icon="fa-stethoscope")
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- # Adding the loading prop for the disease prediction button
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  disease_prediction_result = gr.Markdown(label="Predicted Diseases") # Use Markdown for predictions
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  submit_disease.click(
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  lambda symptom1, symptom2, symptom3, symptom4, symptom5: predict_disease(
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  [symptom1, symptom2, symptom3, symptom4, symptom5]),
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  inputs=[symptom1, symptom2, symptom3, symptom4, symptom5],
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- outputs=disease_prediction_result,
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- loading=True # Enable loading spinner on button click
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- )
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  # Launch the Gradio application
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  app.launch()
 
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  submit_chatbot = gr.Button(value="Submit Your Message", variant="primary", elem_id="submit-chatbot", icon="fa-paper-plane")
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  chatbot = gr.Chatbot(label="Chat History", elem_id="chat-history", show_label=True)
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  sentiment = gr.Textbox(label="Detected Sentiment")
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  emotion = gr.Textbox(label="Detected Emotion")
 
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  professionals = gr.DataFrame(label="Nearby Health Professionals", headers=["Name", "Address"])
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  map_html = gr.HTML(label="Interactive Map")
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+ # Here we add the loading state on the button itself
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  submit_chatbot.click(
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  app_function_chatbot,
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  inputs=[user_input, location, query, chatbot],
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+ outputs=[chatbot, sentiment, emotion, suggestions_markdown, professionals, map_html]
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+ ).then(submit_chatbot.update(loading=True))
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+
 
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  with gr.Tab("Disease Prediction"):
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  symptom1 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 1")
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  symptom2 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 2")
 
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  submit_disease = gr.Button(value="Predict Disease", variant="primary", icon="fa-stethoscope")
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  disease_prediction_result = gr.Markdown(label="Predicted Diseases") # Use Markdown for predictions
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  submit_disease.click(
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  lambda symptom1, symptom2, symptom3, symptom4, symptom5: predict_disease(
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  [symptom1, symptom2, symptom3, symptom4, symptom5]),
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  inputs=[symptom1, symptom2, symptom3, symptom4, symptom5],
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+ outputs=disease_prediction_result
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+ ).then(submit_disease.update(loading=True))
 
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  # Launch the Gradio application
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  app.launch()