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import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration
# xl size run out of memory on 16GB vm
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
title = ""
def get_examples ():
return [
["Being a Happier and Healthier Person"],
["Learn to Use Mindfulness to Affect Well Being"],
["Eating and Drinking - Find Healthy Nutrition Habits"],
["Drinking - Find Reasons and Cut Back or Quit Entirely"],
["Stress is relieved by quieting your mind, getting exercise and time with nature"],
["Reprogram Pain Stress Reactions"],
["Brain gamification"],
["Mental Body Scan"],
["Stretch, Calm, Breath"],
["Relaxed Seat Breath"],
["Walk Feel"],
["alleviating stress"],
["helping breathing, satisfaction"],
["Relieve Stress, Build Support"],
["Relaxation Response"],
["Deep Breaths"],
["Delete Not Helpful Thoughts"],
["Strengthen Helpful"],
["Sleep Better and Find Joy"],
["Yoga Sleep"],
["Relieve Pain"],
["Build and Boost Mental Strength"],
["Spending Time Outdoors"],
["Daily Routine Tasks"],
["Feel better each day when you awake by"],
["Feel better physically by"],
["Practicing mindfulness each day"],
["Be happier by"],
["Meditation can improve health"],
["Spending time outdoors"],
["Break the cycle of stress and anxiety"],
["Feel calm in stressful situations"],
["Deal with work pressure"],
["Learn to reduce feelings of overwhelmed"]
]
def text2text(input_text):
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=200)
return tokenizer.decode(outputs[0])
with gr.Blocks() as demo:
gr.Markdown(
"""
# Flan T5 Large Demo
780M parameter Large language model fine tuned on diverse tasks.
Prompt the model in the Input box.
""")
txt_in = gr.Textbox(label="Input", lines=3)
correct_label = gr.Label(label="Correct")
txt_out = gr.Textbox(value="", label="Output", lines=4)
btn = gr.Button(value="Submit")
btn.click(text2text, inputs=[txt_in], outputs=[txt_out])
gr.Examples(
examples=get_examples(),
inputs=[txt_in,correct_label]
)
if __name__ == "__main__":
demo.launch() |