Update app3.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()

app3.py

@@ -1,61 +0,0 @@
-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()