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import gradio as gr
from transformers import pipeline
# Load the NLP pipeline for text classification
classifier = pipeline("text-classification")
# Define the function to generate mini-apps based on user input
def generate_mini_apps(theme):
# Use the NLP pipeline to classify the input theme
classification = classifier(theme)
# Generate a set of mini-apps based on the classification
if classification[0]['label'] == 'Productivity':
mini_apps = [
'Idea-to-Codebase Generator',
'Automated GitHub Repo Guardian Angel',
'AI-Powered IDE'
]
elif classification[0]['label'] == 'Creativity':
mini_apps = [
'Brainstorming Assistant',
'Mood Board Generator',
'Writing Assistant'
]
elif classification[0]['label'] == 'Well-being':
mini_apps = [
'Meditation Guide',
'Mood Tracker',
'Sleep Tracker'
]
# Return the generated mini-apps
return mini_apps
# Create the Gradio interface
demo = gr.Interface(
fn=generate_mini_apps,
inputs=gr.Textbox(label="Enter a theme for your life"),
outputs=gr.Textbox(label="Generated Mini-Apps"),
title="AI4ME: Personalized AI Tools",
description="Enter a theme for your life and we'll generate a set of AI-powered mini-apps tailored to your specific needs."
)
# Launch the Gradio app
demo.launch() |