app.py

import gradio as gr
from transformers import pipeline
from transformers import AutoModelForCausalLM



# 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()