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import io
import base64
import gradio as gr
from PIL import Image
from openai import OpenAI
def run_demo():
"""Setup the app interface and launch it."""
with gr.Blocks() as app:
gr.Markdown('# Mental Health Nudging with Generative AI Demo')
with gr.Row():
# input features
with gr.Column(scale=2):
# demographics
gender = gr.Radio(label='Gender', value='N/A',
choices=['Male', 'Female', 'Non-Binary', 'N/A'])
age = gr.Slider(label='Age', minimum=18, maximum=80, step=1)
race = gr.Radio(label='Race', value='N/A',
choices=['White', 'Hispanic', 'Black', 'Asian', 'N/A'])
# symptoms
disorders = ['Sadness', 'Inability to concentrate', 'Anxiety', 'Extreme mood changes',
'Social withdrawal', 'Tiredness', 'Lack of appetite', 'Increased appetite']
symptoms = gr.CheckboxGroup(label='Symptoms', choices=disorders)
# interests
interests = gr.Textbox(label='Interests', placeholder='Comma-separated list of interests...')
# submit button
submit_button = gr.Button('Generate Nudge')
# resulting nudge
with gr.Column(scale=1):
nudge_image = gr.Image(label='Nudge Image')
nudge_message = gr.Textbox(label='Nudge Message')
# submit parameters for nudge generation
inputs = [gender, age, race, interests, symptoms]
outputs = [nudge_image, nudge_message]
submit_button.click(fn=generate, inputs=inputs, outputs=outputs)
# launch the app
gr.close_all()
app.queue(default_concurrency_limit=None)
app.launch()
def generate(gender, age, race, interests, symptoms):
"""Generate nudging image and message for the given person."""
nudge_message = generate_nudge_message(gender, age, interests, symptoms)
nudge_image = generate_nudge_image(gender, age, race, nudge_message)
return nudge_image, nudge_message
def generate_nudge_message(gender, age, interests, symptoms):
"""Generate a message for a given person."""
# construct description of the person
desc = f'A {age} year old '
if gender == 'Male':
desc += 'man.'
elif gender == 'Female':
desc += 'woman.'
elif gender == 'Non-Binary':
desc += 'non-binary person.'
else:
desc += 'person.'
if interests:
desc += f' They like {interests}.'
if symptoms:
desc += f' They have the following mental health symptoms: {", ".join(map(str.lower, symptoms))}.'
else:
desc += f' They do not have any mental health symptoms.'
# generate nudge message
system_prompt = 'You are writing motivational text messages to help people with their mental health. '\
+ 'Messages should be friendly and positive, but also professional and super short. '\
+ 'You are limited on space. Messages should be written at the reading level of an eighth grader. '\
+ 'Word choice should be short and simple so everyone can understand. \n\n'\
+ 'You will be given some basic information about the person you are addressing. '\
+ 'Messages should be short, so be discerning. You should try to use the person\'s '\
+ 'information to give them relevant and actionable tips for improving their mental health symptoms.'
user_prompt = f'Write a short inspirational message for the person with the following description:\n\n{desc}'
messages = [{'role': 'system', 'content': f'{system_prompt}'},
{'role': 'user', 'content': f'{user_prompt}'}]
completion = client.chat.completions.create(messages=messages, model='gpt-3.5-turbo', temperature=.5)
nudge_message = completion.choices[0].message.content
return nudge_message
def generate_nudge_image(gender, age, race, nudge_message):
"""Generate an image for a given person and message."""
# construct description of the person
desc = f'a {age} year old '
if race != 'N/A':
desc += f'{race.lower()} '
if gender == 'Male':
desc += 'man.'
elif gender == 'Female':
desc += 'woman.'
elif gender == 'Non-Binary':
desc += 'non-binary person.'
else:
desc += 'person.'
# generate nudge image
prompt = 'Illustrate one simple, inspirational, fun image to help a person with their mental health. NO TEXT. '\
+ f'The style is cute and illustrative. It is focused on {desc} '\
+ f'The image should suit the following message:\n\n{nudge_message}'
response = client.images.generate(prompt=prompt, model='dall-e-3', response_format='b64_json')
nudge_image = Image.open(io.BytesIO(base64.b64decode(response.data[0].b64_json)))
return nudge_image
if __name__ == '__main__':
client = OpenAI()
run_demo()
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