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Update app.py
Browse files
app.py
CHANGED
@@ -10,59 +10,27 @@ def process_answers(
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stress_level,
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wellness_goals,
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dietary_restrictions,
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eating_habits,
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relaxation_time,
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health_issues,
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stress_management,
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daily_routine,
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bedtime_routine,
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water_intake,
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mindfulness_frequency,
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mindset,
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personal_growth_reflection,
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break_frequency,
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relaxation_activities,
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self_care_time,
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outdoor_activity_frequency,
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gratitude_practice,
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home_cooked_meals,
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uninterrupted_sleep,
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gratitude_feelings,
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connection_rating,
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activity_tracking,
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strength_training,
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energy_rating
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):
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#
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responses = {
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questions[0]: sleep,
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questions[1]: exercise,
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questions[2]: mood,
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questions[3]: stress_level,
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questions[4]: wellness_goals,
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questions[5]: dietary_restrictions,
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questions[
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questions[
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questions[
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questions[
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questions[
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questions[
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questions[12]: water_intake,
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questions[13]: mindfulness_frequency,
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questions[14]: mindset,
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questions[15]: personal_growth_reflection,
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questions[16]: break_frequency,
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questions[17]: relaxation_activities,
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questions[18]: self_care_time,
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questions[19]: outdoor_activity_frequency,
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questions[20]: gratitude_practice,
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questions[21]: home_cooked_meals,
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questions[22]: uninterrupted_sleep,
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questions[23]: gratitude_feelings,
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questions[24]: connection_rating,
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questions[25]: activity_tracking,
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questions[26]: strength_training,
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questions[27]: energy_rating
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}
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# Process responses using the centralized pipeline
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@@ -71,7 +39,6 @@ def process_answers(
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# Format outputs using results from the pipeline
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wellness_report = f"**Wellness Report**\n------------------\n{results['report'].strip()}"
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# Updated Identified Problems section to display eight new themes
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identified_problems = (
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"**Identified Problems**\n"
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"-----------------------\n"
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@@ -105,9 +72,23 @@ def process_answers(
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return wellness_report, identified_problems, recommendations, summary_shown, final_summary_video
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iface = gr.Interface(
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fn=process_answers,
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inputs=[
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outputs=[
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gr.Markdown(label="Wellness Report"),
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gr.Markdown(label="Identified Problems"),
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stress_level,
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wellness_goals,
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dietary_restrictions,
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relaxation_time,
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health_issues,
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water_intake,
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gratitude_feelings,
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connection_rating,
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energy_rating
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):
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# Map the selected responses to their corresponding questions
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responses = {
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questions[0]: sleep, # How many hours of sleep do you get each night?
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questions[1]: exercise, # How often do you exercise in a week?
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questions[2]: mood, # On a scale of 1 to 10, how would you rate your mood today?
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questions[3]: stress_level, # What is your current stress level on a scale from 1 to 10?
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questions[4]: wellness_goals, # What are your primary wellness goals?
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questions[5]: dietary_restrictions, # Do you follow any specific diet or have any dietary restrictions?
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questions[7]: relaxation_time, # How much time do you spend on relaxation or mindfulness activities daily?
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questions[8]: health_issues, # Do you experience any recurring health issues or pain?
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questions[12]: water_intake, # How much water do you drink on average per day?
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questions[23]: gratitude_feelings, # How often do you experience feelings of gratitude or happiness?
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questions[24]: connection_rating, # On a scale from 1 to 10, how connected do you feel to your friends and family?
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questions[27]: energy_rating # On a scale from 1 to 10, how would you rate your energy levels throughout the day?
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}
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# Process responses using the centralized pipeline
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# Format outputs using results from the pipeline
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wellness_report = f"**Wellness Report**\n------------------\n{results['report'].strip()}"
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identified_problems = (
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"**Identified Problems**\n"
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"-----------------------\n"
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return wellness_report, identified_problems, recommendations, summary_shown, final_summary_video
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# Define the Gradio interface using only the 12 selected questions
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iface = gr.Interface(
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fn=process_answers,
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inputs=[
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gr.Textbox(label=questions[0]),
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gr.Textbox(label=questions[1]),
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gr.Textbox(label=questions[2]),
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gr.Textbox(label=questions[3]),
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gr.Textbox(label=questions[4]),
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gr.Textbox(label=questions[5]),
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gr.Textbox(label=questions[7]),
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gr.Textbox(label=questions[8]),
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gr.Textbox(label=questions[12]),
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gr.Textbox(label=questions[23]),
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gr.Textbox(label=questions[24]),
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gr.Textbox(label=questions[27])
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],
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outputs=[
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gr.Markdown(label="Wellness Report"),
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gr.Markdown(label="Identified Problems"),
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