Update app3.py

app3.py

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