Update app.py

app.py

@@ -1,34 +1,51 @@
 import streamlit as st
-import matplotlib.pyplot as plt
-import plotly.express as px
 import pandas as pd
+import plotly.express as px
+import matplotlib.pyplot as plt
+import seaborn as sns
 
-# Load your data (replace with your actual dataset)
-df = pd.read_csv('your_data.csv')
+# Load your dataset here
+df = pd.read_csv('your_data.csv')  # Replace with your actual dataset file
 
-# Create an interactive scatter plot
-fig = px.scatter(df, x='column_x', y='column_y', color='category_column', title="Interactive Scatter Plot")
+# Streamlit Interface for Plotting Scatter Plot and Simple Chart
+def streamlit_interface():
+    st.title("Interactive Data Visualization App")
+
+    # Display dataframe
+    st.write(df.head())
+
+    # Plot interactive scatter plot with Plotly
+    scatter_fig = px.scatter(df, x='column_x', y='column_y', color='category_column', title="Interactive Scatter Plot")
+    st.plotly_chart(scatter_fig)
+
+    # Input fields for custom plotting
+    x_values = st.text_input("Enter X values (comma-separated)", "1,2,3,4,5")
+    y_values = st.text_input("Enter Y values (comma-separated)", "2,4,6,8,10")
 
-# Show the plot
-fig.show()
+    # Button to plot custom chart
+    if st.button("Plot Custom Chart"):
+        plot_custom_chart(x_values, y_values)
 
+    # Correlation Heatmap using Seaborn
+    st.subheader("Correlation Heatmap")
+    plot_correlation_heatmap(df)
 
-# Function to plot the chart
-def plot_chart(x_values, y_values):
+# Function to plot custom chart
+def plot_custom_chart(x_values, y_values):
     try:
         # Convert the X and Y values from string input to lists of integers
         x_vals = list(map(int, x_values.split(',')))
         y_vals = list(map(int, y_values.split(',')))
-        
+
         # Ensure both X and Y values have the same length
         if len(x_vals) != len(y_vals):
             st.error("Error: X and Y values must have the same number of elements.")
             return
-        
-        # Create a plot
+
+        # Plot using Matplotlib
         plt.figure(figsize=(8, 5))
         plt.plot(x_vals, y_vals, marker='o', color='b', label="Data Points")
-        plt.title("Data Visualization")
+        plt.title("Custom Data Visualization")
         plt.xlabel("X Values")
         plt.ylabel("Y Values")
         plt.grid(True)
@@ -40,91 +57,16 @@ def plot_chart(x_values, y_values):
     except ValueError:
         st.error("Error: Please make sure the values are valid integers.")
 
-# Streamlit interface
-def main():
-    st.title("Data Visualization App")
-    
-    # Input fields for X and Y values
-    x_values = st.text_input("Enter X values (comma-separated)", "1,2,3,4,5")
-    y_values = st.text_input("Enter Y values (comma-separated)", "2,4,6,8,10")
-
-    # Button to plot the chart
-    if st.button("Plot Chart"):
-        plot_chart(x_values, y_values)
+# Function to plot correlation heatmap
+def plot_correlation_heatmap(df):
+    corr = df.corr()  # Calculate correlation matrix
+    plt.figure(figsize=(10, 8))
+    sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f')
+    plt.title('Correlation Heatmap')
+    st.pyplot(plt)  # Display in Streamlit
 
-# Run the app
+# Run the Streamlit interface
 if __name__ == "__main__":
-    main()
-    import dash
-from dash import dcc, html
-import plotly.express as px
-import pandas as pd
-
-# Load data
-df = pd.read_csv('your_data.csv')
-
-# Create Dash app
-app = dash.Dash(__name__)
-
-# Generate a plotly chart
-fig = px.scatter(df, x='column_x', y='column_y', color='category_column', title="Interactive Scatter Plot")
-
-# Define the layout with a dropdown for filtering
-app.layout = html.Div([
-    html.H1("Interactive Data Visualization"),
-    dcc.Dropdown(
-        id='category-dropdown',
-        options=[{'label': i, 'value': i} for i in df['category_column'].unique()],
-        value=df['category_column'].unique()[0]  # Default value
-    ),
-    dcc.Graph(id='scatter-plot', figure=fig)
-])
-
-# Callback to update figure based on dropdown selection
-@app.callback(
-    dash.dependencies.Output('scatter-plot', 'figure'),
-    [dash.dependencies.Input('category-dropdown', 'value')]
-)
-def update_graph(selected_category):
-    filtered_df = df[df['category_column'] == selected_category]
-    return px.scatter(filtered_df, x='column_x', y='column_y', color='category_column', title="Filtered Scatter Plot")
-
-# Run the app
-if __name__ == '__main__':
-    app.run_server(debug=True)
-
-
-import streamlit as st
-import pandas as pd
-
-# Load your data (use caching to improve performance)
-@st.cache
-def load_data():
-    return pd.read_csv('your_data.csv')
-
-# Load data
-df = load_data()
-
-# Display the data in Streamlit
-st.write(df.head())
-
-# Display a simple plot
-import matplotlib.pyplot as plt
-fig, ax = plt.subplots()
-ax.scatter(df['column_x'], df['column_y'])
-st.pyplot(fig)
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-# Generate a correlation matrix
-corr = df.corr()
-
-# Create a heatmap
-plt.figure(figsize=(10, 8))
-sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f')
-plt.title('Correlation Heatmap')
-
-# Display the heatmap
-plt.show()
+    streamlit_interface()