Create App.py

App.py

@@ -0,0 +1,39 @@
+import streamlit as st
+import pandas as pd
+
+# Load the data
+@st.cache
+def load_data():
+    # Update this path to point to your local file if you're running it outside Kaggle
+    data = pd.read_csv('/kaggle/input/web-crawler-for-real-estate-market/Output.csv')
+    return data
+
+# Display the title and description of the app
+st.title("Real Estate Market Analysis")
+st.write("""
+    This Streamlit app displays and analyzes real estate data.
+""")
+
+# Load the data
+data = load_data()
+
+# Show the first few rows of the dataframe
+st.write("### Data Preview")
+st.dataframe(data.head())
+
+# Allow the user to filter the data
+st.sidebar.header("Filter Data")
+selected_columns = st.sidebar.multiselect("Select columns to display", data.columns.tolist(), default=data.columns.tolist())
+filtered_data = data[selected_columns]
+st.write("### Filtered Data")
+st.dataframe(filtered_data)
+
+# Add any other analysis or features you want to include
+st.sidebar.header("Statistics")
+st.write("### Summary Statistics")
+st.write(data.describe())
+
+# Display a histogram of a selected column
+column = st.sidebar.selectbox("Select column for histogram", data.columns.tolist())
+st.write(f"### Histogram of {column}")
+st.bar_chart(data[column].value_counts())