Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.graph_objs as go
+import plotly.figure_factory as ff
+
+def main():
+    st.title("Bar Chart and 3D Graph Example")
+
+    # Generate some sample data for the bar chart
+    bar_data = pd.DataFrame({
+        'Category': ['A', 'B', 'C', 'D'],
+        'Values': [10, 20, 15, 25]
+    })
+
+    # Display the data for the bar chart
+    st.write("Sample Data for Bar Chart:")
+    st.write(bar_data)
+
+    # Create a bar chart
+    st.write("Bar Chart:")
+    fig_bar = go.Figure(data=[go.Bar(x=bar_data['Category'], y=bar_data['Values'])])
+    st.plotly_chart(fig_bar)
+
+    # Generate some sample data for the 3D surface plot
+    x = np.linspace(-5, 5, 100)
+    y = np.linspace(-5, 5, 100)
+    X, Y = np.meshgrid(x, y)
+    Z = np.sin(np.sqrt(X**2 + Y**2))
+
+    # Create a DataFrame for the 3D surface plot
+    surface_data = pd.DataFrame({'X': X.flatten(), 'Y': Y.flatten(), 'Z': Z.flatten()})
+
+    # Display the data for the 3D surface plot
+    st.write("Sample Data for 3D Surface Plot:")
+    st.write(surface_data.head())
+
+    # Create a 3D surface plot
+    st.write("3D Surface Plot:")
+    fig_surface = go.Figure(data=[go.Surface(z=surface_data['Z'].values.reshape(100, 100),
+                                             x=surface_data['X'].values.reshape(100, 100),
+                                             y=surface_data['Y'].values.reshape(100, 100))])
+    st.plotly_chart(fig_surface)
+
+    # Generate some sample data for the confusion matrix
+    confusion_matrix_data = np.array([[30, 10], [5, 55]])
+
+    # Create a DataFrame for the confusion matrix
+    cm_df = pd.DataFrame(confusion_matrix_data, columns=['Predicted Negative', 'Predicted Positive'], index=['Actual Negative', 'Actual Positive'])
+
+    # Display the data for the confusion matrix
+    st.write("Confusion Matrix Data:")
+    st.write(cm_df)
+
+    # Create a confusion matrix graph
+    st.write("Confusion Matrix:")
+    fig_cm = ff.create_annotated_heatmap(z=confusion_matrix_data, x=['Predicted Negative', 'Predicted Positive'], y=['Actual Negative', 'Actual Positive'], colorscale='Viridis')
+    fig_cm.update_layout(title="Confusion Matrix", xaxis_title="Predicted Label", yaxis_title="Actual Label")
+    st.plotly_chart(fig_cm)
+
+    # Generate some sample data for the heatmap
+    np.random.seed(0)
+    data = np.random.rand(10, 10)
+
+    # Create a DataFrame for the heatmap
+    heatmap_data = pd.DataFrame(data)
+
+    # Display the data for the heatmap
+    st.write("Sample Data for Heatmap:")
+    st.write(heatmap_data.head())
+
+    # Create a heatmap
+    st.write("Heatmap:")
+    fig_heatmap = go.Figure(data=go.Heatmap(z=data))
+    st.plotly_chart(fig_heatmap)
+
+    # Generate some sample data for the histogram
+    np.random.seed(0)
+    data = np.random.randn(1000)
+
+    # Create a DataFrame for the histogram
+    hist_data = pd.DataFrame({'Values': data})
+
+    # Display the data for the histogram
+    st.write("Sample Data for Histogram:")
+    st.write(hist_data.head())
+
+    # Create a histogram
+    st.write("Histogram:")
+    fig_hist = go.Figure(data=[go.Histogram(x=hist_data['Values'])])
+    st.plotly_chart(fig_hist)
+
+    # Generate some sample data for the scatter plot
+    np.random.seed(0)
+    x = np.random.randn(100)
+    y = np.random.randn(100)
+
+    # Create a DataFrame for the scatter plot
+    scatter_data = pd.DataFrame({'X': x, 'Y': y})
+
+    # Display the data for the scatter plot
+    st.write("Sample Data for Scatter Plot:")
+    st.write(scatter_data.head())
+
+    # Create a scatter plot
+    st.write("Scatter Plot:")
+    fig_scatter = go.Figure(data=[go.Scatter(x=scatter_data['X'], y=scatter_data['Y'], mode='markers')])
+    st.plotly_chart(fig_scatter)
+
+if __name__ == "__main__":
+    main()