app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns

# Load your dataset here
df = pd.read_csv('your_data.csv')  # Replace with your actual dataset file

# 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")

    # 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 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

        # Plot using Matplotlib
        plt.figure(figsize=(8, 5))
        plt.plot(x_vals, y_vals, marker='o', color='b', label="Data Points")
        plt.title("Custom Data Visualization")
        plt.xlabel("X Values")
        plt.ylabel("Y Values")
        plt.grid(True)
        plt.legend()

        # Display the plot
        st.pyplot(plt)

    except ValueError:
        st.error("Error: Please make sure the values are valid integers.")

# 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 Streamlit interface
if __name__ == "__main__":
    streamlit_interface()