Update app.py

app.py

@@ -1,50 +1,76 @@
 import streamlit as st
 from datasets import load_dataset
-import streamlit.components.v1 as components
+import pandas as pd
+import json
+import os
 
-# Load the dataset
+# Load the dataset and convert it to a pandas DataFrame
 dataset = load_dataset("awacke1/DatasetOfDatasetsUSA")
+df = pd.DataFrame(dataset['train'])
 
-# Initialize session state for record navigation
+# Path to the file where likes are stored
+likes_file_path = 'likes_data.json'
+
+# Load or initialize likes history
+if os.path.exists(likes_file_path):
+    with open(likes_file_path, 'r') as file:
+        likes_history = json.load(file)
+else:
+    likes_history = {}
+
+# Define a function to save likes history
+def save_likes_history():
+    with open(likes_file_path, 'w') as file:
+        json.dump(likes_history, file)
+
+# Define a function to update likes
+def update_likes(index):
+    if index in likes_history:
+        likes_history[index] += 1
+    else:
+        likes_history[index] = 1
+    save_likes_history()
+    st.experimental_rerun()
+
+# Sidebar for search
+with st.sidebar:
+    search_query = st.text_input("🔍 Search", "")
+    search_button = st.button("Search")
+
+# Filter DataFrame based on search query
+if search_query:
+    filtered_df = df[df.apply(lambda row: search_query.lower() in row.to_string().lower(), axis=1)]
+else:
+    filtered_df = df
+
+# Display search results or full DataFrame
+start_index = 0  # Start from the first record; adjust based on pagination if implemented
+display_limit = 10  # Number of records to display at a time; adjust as needed
+
+# Pagination setup
 if 'index' not in st.session_state:
     st.session_state.index = 0
 
-# Define the maximum index as the length of the dataset - 1
-max_index = len(dataset['train']) - 1
-
-# Navigation buttons
-col1, col2, col3, col4, col5 = st.columns(5)
-with col1:
-    if st.button('⏮️'):
-        st.session_state.index = 0
-with col2:
-    if st.button('◀️') and st.session_state.index > 0:
-        st.session_state.index -= 1
-with col3:
-    st.write(f"Record {st.session_state.index + 1} of {max_index + 1}")
-with col4:
-    if st.button('▶️') and st.session_state.index < max_index:
-        st.session_state.index += 1
-with col5:
-    if st.button('⏭️'):
-        st.session_state.index = max_index
-
-# Assuming the dataset has the columns 'cityOrState', 'link', and 'linkType'
-item = dataset['train'][st.session_state.index]
-cityOrState = item['cityOrState']
-link = item['link']
-linkType = item['linkType']
-
-# Build the HTML for the current record
-links_html = f"""
-<ul style="list-style: none; padding: 0;">
-    <li style="margin: 10px 0; padding: 10px; background-color: #f0f0f0; border-radius: 8px; box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);">
-        <a href="{link}" target="_blank" style="text-decoration: none; color: #333;">
-            <strong>{cityOrState}</strong> - {linkType} 🔗
-        </a>
-    </li>
-</ul>
-"""
-
-# Use Streamlit components to render the HTML
-components.html(links_html, height=100)
+# Display records with pagination
+for i in range(st.session_state.index, min(st.session_state.index + display_limit, len(filtered_df))):
+    item = filtered_df.iloc[i]
+    cityOrState, link, linkType = item['cityOrState'], item['link'], item['linkType']
+    liked = likes_history.get(str(i), 0)
+
+    with st.expander(f"{cityOrState} - {linkType} 🔗"):
+        st.markdown(f"[{link}]({link})")
+        like_button = st.button("👍 Like", key=f"like_{i}")
+        if like_button:
+            update_likes(str(i))
+
+# Navigation buttons for pagination
+prev, _, next = st.columns([1,10,1])
+if prev.button("Previous"):
+    st.session_state.index = max(0, st.session_state.index - display_limit)
+if next.button("Next") and st.session_state.index + display_limit < len(filtered_df):
+    st.session_state.index += display_limit
+
+
+
+
+