Update pages/comps_data.py

pages/comps_data.py

@@ -1,51 +1,110 @@
 import streamlit as st
 import pandas as pd
 
-# Sample DataFrame
 data = {
-    'Name': ['John', 'Alice', 'Bob', 'Emma'],
-    'Age': [30, 25, 35, 28],
-    'Location': ['New York', 'San Francisco', 'Los Angeles', 'Chicago']
+    'Address': ['Location_A', 'Location_B', 'Location_C', 'Location_D', 'Location_E',
+            'Location_F', 'Location_G', 'Location_H'
+            ],
+    'latitude': np.random.uniform(40.7, 40.8, size=8),  # Assuming latitude range between 40.7 and 40.8
+    'longitude': np.random.uniform(-74.0, -73.9, size=8),  # Assuming longitude range between -74.0 and -73.9
+    'Match score': [90, 89, 88, 87, 86, 85, 84, 83],
+    'Market': ["M1", "M1", "M1", "M1", "M1", "M1", "M1", "M1"],
+    'Sub-market': ["S1", "S1", "S1", "S1", "S1", "S1", "S1", "S1"],
+    'Lease Date': ["2024/1/1", "2024/1/1", "2024/1/1", "2024/1/1", "2024/1/1", "2024/1/1", "2024/1/1", "2024/1/1"],
+    'LSF': [20000, 30000, 20000, 30000, 20000, 30000, 50000, 35000],
+    'RSF': [20000, 30000, 20000, 30000, 20000, 30000, 50000, 35000],
+    'Rent (NNN)': [11, 11, 11, 12, 12, 12, 12, 15],
+    'Year Built': [2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019],
+    'Office %': [20, 20, 20, 20, 20, 20, 20, 20],
+    'Clear Height':[19, 18, 19, 18, 17, 19, 19, 18],
+    'Doors (drive in / Dock)': [2, 2, 2, 2, 2, 2, 2, 2],
+    'Lease Term ': [60, 60, 60, 60, 60, 60, 60, 60],
+    'Rent (Gross)': [11, 11, 11, 12, 12, 12, 12, 15],
+    'TIs ': [1, 1, 1, 1, 1, 1,  1, 1]
 }
-df = pd.DataFrame(data)
 
-# Function to create the map section
-def create_map():
-    # You can customize this map as per your data
-    st.subheader('Map')
-    st.map(df)
+# Create DataFrame
+df_data =  pd.DataFrame(data)
+filtered_data = df_data[['Address', 'Market', 'Sub-market', 'Lease Date', 'LSF', 'RSF', 'Rent (NNN)', 'Year Built', 'Office %', 'Clear Height', 'Doors (drive in / Dock)', 'Lease Term ', 'Rent (Gross)']]
 
-# Function to create the list section with checkboxes
-def create_checkbox_list():
-    st.subheader('List with Checkboxes')
-    for index, row in df.iterrows():
-        if st.checkbox(row['Name']):
-            st.write(row)
+#New property
+# st.write('Target property:')
+# data_new = {
+#     'Address': ['Target property'
+#             ],
+#     # 'latitude': np.random.uniform(40.7, 40.8, size=1),  # Assuming latitude range between 40.7 and 40.8
+#     # 'longitude': np.random.uniform(-74.0, -73.9, size=1),  # Assuming longitude range between -74.0 and -73.9
+#     'Market': ["M1"],
+#     'Sub-market': ["S1"],
+#     'Lease Date': ["2024/1/1"],
+#     'LSF': [20000],
+#     'RSF': [20000],
+#     'Rent (NNN)': [""],
+#     'Year Built': [2019],
+#     'Office %': [20],
+#     'Clear Height':[18],
+#     'Doors (drive in / Dock)': [2],
+#     'Lease Term ': [""],
+#     'Rent (Gross)': [""],
+#     # 'TIs ': []
+# }
+# new_preperty_data =  pd.DataFrame(data_new)
+# st.write(new_preperty_data)
+# st.write("")
 
-# Function to create the filter section
-def create_filters():
-    st.subheader('Filters')
-    # Define your filters here, you can use sliders, selectbox, etc.
-    min_age = st.slider('Minimum Age', min_value=20, max_value=50, value=20)
-    max_age = st.slider('Maximum Age', min_value=20, max_value=50, value=50)
-    st.write(f'Filtered Data: Age between {min_age} and {max_age}')
-    filtered_df = df[(df['Age'] >= min_age) & (df['Age'] <= max_age)]
-    st.write(filtered_df)
-
-# Layout
-st.title('Streamlit App with Multiple Sections')
-
-# Section 1 - List in the right top corner
-st.subheader('List with Checkboxes (Right Top Corner)')
-create_checkbox_list()
-
-# Section 2 - Map in the left top corner
-st.subheader('Map (Left Top Corner)')
-create_map()
-
-# Section 3 - List in the right bottom corner
-st.subheader('List with Checkboxes (Right Bottom Corner)')
-create_checkbox_list()
-
-# Section 4 - Filters
-create_filters()
+# Display the filtered data
+col_1_1, col_1_2 = st.columns([2, 1])
+with col_1_1:
+    st.write('Comps list:')
+    st.write(filtered_data)
+with col_1_2:
+    # Create a map object
+    m = folium.Map(location=(df_data['latitude'].mean(), df_data['longitude'].mean()), zoom_start=10)
+    
+    # Add markers to the map
+    all_markers = folium.FeatureGroup(name='All Markers')
+    active_markers = folium.FeatureGroup(name='Active Markers', show=False)
+    inactive_markers = folium.FeatureGroup(name='Inactive Markers', show=False)
+    
+    
+    for index, row in df_data.iterrows():
+        status_color = 'green' if index%2==0 else 'red'
+        html_content = f"""
+        <div style="
+            display: inline-block;
+            background-color: white;
+            border: 2px solid black;
+            border-radius: 50%;
+            width: 20px;
+            height: 20px;
+            text-align: center;
+            line-height: 20px;
+            font-size: 8pt;
+            color: {status_color};
+        ">{index}</div>
+        """
+    
+        # Create a DivIcon with custom HTML content
+        icon = folium.DivIcon(html=html_content)
+        marker = folium.Marker([row['latitude'], row['longitude']], popup=row['Address'], icon=icon).add_to(m)
+        
+    
+    # Add layer control to toggle marker visibility
+    folium.LayerControl().add_to(m)
+    
+    polygon_coords = [ [np.random.uniform(40.7, 40.8, size=6)[i], np.random.uniform(-74.0, -73.9, size=6)[i]] for i in range(6)]
+    
+    # Create a polygon object
+    polygon = folium.Polygon(locations=polygon_coords, color='blue', fill=True, fill_color='blue', fill_opacity=0.3)
+    
+    # Add the tag to the polygon 
+    popup_text = "This is my polygon"
+    popup = folium.Popup(popup_text, parse_html=True)
+    polygon.add_child(popup)
+    
+    # Add the polygon to the map
+    m.add_child(polygon)
+    
+        
+    # Render the map
+    folium_static(m)