# app.py
import gradio as gr
from pipeline import process_answers_pipeline  # Import the centralized pipeline function
from questions import questions  # Import questions list

def process_answers(
    sleep,
    exercise,
    mood,
    stress_level,
    wellness_goals,
    dietary_restrictions,
    relaxation_time,
    health_issues,
    water_intake,
    gratitude_feelings,
    connection_rating,
    energy_rating
):
    # Map the selected responses to their corresponding questions
    responses = {
        questions[0]: sleep,                     # How many hours of sleep do you get each night?
        questions[1]: exercise,                  # How often do you exercise in a week?
        questions[2]: mood,                      # On a scale of 1 to 10, how would you rate your mood today?
        questions[3]: stress_level,              # What is your current stress level on a scale from 1 to 10?
        questions[4]: wellness_goals,            # What are your primary wellness goals?
        questions[5]: dietary_restrictions,      # Do you follow any specific diet or have any dietary restrictions?
        questions[7]: relaxation_time,           # How much time do you spend on relaxation or mindfulness activities daily?
        questions[8]: health_issues,             # Do you experience any recurring health issues or pain?
        questions[12]: water_intake,             # How much water do you drink on average per day?
        questions[23]: gratitude_feelings,       # How often do you experience feelings of gratitude or happiness?
        questions[24]: connection_rating,        # On a scale from 1 to 10, how connected do you feel to your friends and family?
        questions[27]: energy_rating             # On a scale from 1 to 10, how would you rate your energy levels throughout the day?
    }

    # Process responses using the centralized pipeline
    results = process_answers_pipeline(responses)

    # Format outputs using results from the pipeline
    wellness_report = f"**Wellness Report**\n------------------\n{results['report'].strip()}"

    identified_problems = (
        "**Identified Problems**\n"
        "-----------------------\n"
        f"Stress Management: {results['problems'].get('stress_management', 'N/A')}%\n"
        f"Low Therapy: {results['problems'].get('low_therapy', 'N/A')}%\n"
        f"Balanced Weight: {results['problems'].get('balanced_weight', 'N/A')}%\n"
        f"Restless Night: {results['problems'].get('restless_night', 'N/A')}%\n"
        f"Lack of Motivation: {results['problems'].get('lack_of_motivation', 'N/A')}%\n"
        f"Gut Health: {results['problems'].get('gut_health', 'N/A')}%\n"
        f"Anxiety: {results['problems'].get('anxiety', 'N/A')}%\n"
        f"Burnout: {results['problems'].get('burnout', 'N/A')}%"
    )

    recommendations = (
        "**Recommendations**\n"
        "--------------------\n"
        f"{results['recommendation'].strip()}"
    )

    summary_shown = (
        "**Summary (SHOWN TO USER)**\n"
        "-----------------\n"
        f"{results['final_summary'].strip()}"
    )

    final_summary_video = (
        "**Final Summary (FOR VIDEO CREATION)**\n"
        "-----------------\n"
        f"{results['shortened_summary'].strip()}"
    )

    return wellness_report, identified_problems, recommendations, summary_shown, final_summary_video

# Define the Gradio interface using only the 12 selected questions
iface = gr.Interface(
    fn=process_answers,
    inputs=[
        gr.Textbox(label=questions[0]),
        gr.Textbox(label=questions[1]),
        gr.Textbox(label=questions[2]),
        gr.Textbox(label=questions[3]),
        gr.Textbox(label=questions[4]),
        gr.Textbox(label=questions[5]),
        gr.Textbox(label=questions[7]),
        gr.Textbox(label=questions[8]),
        gr.Textbox(label=questions[12]),
        gr.Textbox(label=questions[23]),
        gr.Textbox(label=questions[24]),
        gr.Textbox(label=questions[27])
    ],
    outputs=[
        gr.Markdown(label="Wellness Report"),
        gr.Markdown(label="Identified Problems"),
        gr.Markdown(label="Recommendations"),
        gr.Markdown(label="Summary (SHOWN TO USER)"),
        gr.Markdown(label="Final Summary (FOR VIDEO CREATION)")
    ],
    title="Wellness Report Generator",
    description="Answer the questions to generate a wellness report, problem analysis, recommendations, and a final summary."
)

iface.launch()