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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()
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