app.py

import gradio as gr
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Sample Data (REPLACE WITH YOUR ACTUAL DATABASE)
data = {
    'event_id': [101, 102, 103, 104, 105],
    'title': ['Hiking Meetup', 'Book Club Discussion', 'Gardening Workshop', 'Coding Class', 'Yoga Session'],
    'description': ['Explore local trails', 'Discuss "The Great Gatsby"', 'Learn basic gardening', 'Python programming basics', 'Relaxing yoga practice'],
    'tags': ['hiking, nature, outdoors', 'books, literature, reading', 'gardening, plants, nature', 'coding, programming, python', 'yoga, fitness, relaxation']
}
events_df = pd.DataFrame(data)

def recommend_activities(interests, num_recommendations=3):
    if not interests:
        return "Please enter your interests."

    user_interests = interests.lower()
    tfidf = TfidfVectorizer()
    tfidf_matrix_events = tfidf.fit_transform(events_df['tags'])
    tfidf_matrix_user = tfidf.transform([user_interests])
    similarities = cosine_similarity(tfidf_matrix_user, tfidf_matrix_events)
    top_indices = similarities.argsort()[0][-num_recommendations:][::-1]
    recommendations = events_df.iloc[top_indices][['title', 'description']].values.tolist()
    return recommendations


def create_event(title, description, location, date_time, tags):
    print(f"Event created: {title}, {description}, {location}, {date_time}, {tags}")
    return "Event created successfully!"


with gr.Blocks() as demo:
    gr.Markdown("# AI Community Builder")

    with gr.Row():
        with gr.Column():
            interests_input = gr.Textbox(label="Your Interests (comma-separated)", lines=2, placeholder="e.g., hiking, reading, cooking")
            num_recs_input = gr.Slider(label="Number of Recommendations", minimum=1, maximum=5, value=3, step=1)
            recommend_button = gr.Button("Get Recommendations")

        with gr.Column():
            recommendations_output = gr.Dataframe(headers=["Title", "Description"], datatype=["str", "str"], interactive=True)


    recommend_button.click(fn=recommend_activities, inputs=[interests_input, num_recs_input], outputs=recommendations_output)

    with gr.Accordion("Create a New Event", open=False):
        with gr.Row():
            title_input = gr.Textbox(label="Event Title", placeholder="Enter event title")
            description_input = gr.Textbox(label="Description", lines=3, placeholder="Enter a brief description")
        with gr.Row():
            location_input = gr.Textbox(label="Location", placeholder="Enter location details")
            datetime_input = gr.Textbox(label="Date & Time (YYYY-MM-DD HH:MM)", placeholder="Enter date and time")
        tags_input = gr.Textbox(label="Tags (comma-separated)", placeholder="Enter relevant tags")
        create_event_button = gr.Button("Create Event")

        create_event_button.click(fn=create_event, inputs=[title_input, description_input, location_input, datetime_input, tags_input], outputs=gr.Textbox(label="Result"))

demo.launch()