Create app.py

app.py

@@ -0,0 +1,138 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+# Sidebar
+st.sidebar.header("Select Visualization")
+plot_type = st.sidebar.selectbox("Choose a plot type", ("Heatmap", "3D Heatmap", "Contour", "Quiver", "Contourf", "Streamplot", "Hexbin", "Eventplot", "Tricontour", "Triplot", "3D Voxel"))
+
+# Load Data
+data = pd.read_csv("healthcare_treatments.csv")
+
+# Define Functions for each plot type
+def heatmap():
+    fig, ax = plt.subplots()
+    ax.set_title("Top Health Care Treatments")
+    heatmap_data = np.random.rand(10, 10)
+    im = ax.imshow(heatmap_data, cmap="YlOrRd")
+    plt.colorbar(im, ax=ax)
+    st.pyplot(fig)
+
+def heatmap_3d():
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection='3d')
+    ax.set_title("Top Health Care Treatments")
+    x, y = np.meshgrid(range(10), range(10))
+    z = np.random.rand(10, 10)
+    ax.plot_surface(x, y, z, cmap="YlOrRd")
+    st.pyplot(fig)
+
+def contour():
+    fig, ax = plt.subplots()
+    ax.set_title("Top Health Care Treatments")
+    x = np.linspace(-3, 3, 100)
+    y = np.linspace(-3, 3, 100)
+    X, Y = np.meshgrid(x, y)
+    Z = np.sin(np.sqrt(X**2 + Y**2))
+    ax.contour(X, Y, Z, cmap="YlOrRd")
+    st.pyplot(fig)
+
+def quiver():
+    fig, ax = plt.subplots()
+    ax.set_title("Top Health Care Treatments")
+    x = np.arange(-2, 2, 0.2)
+    y = np.arange(-2, 2, 0.2)
+    X, Y = np.meshgrid(x, y)
+    U = np.cos(X)
+    V = np.sin(Y)
+    ax.quiver(X, Y, U, V)
+    st.pyplot(fig)
+
+def contourf():
+    fig, ax = plt.subplots()
+    ax.set_title("Top Health Care Treatments")
+    x = np.linspace(-3, 3, 100)
+    y = np.linspace(-3, 3, 100)
+    X, Y = np.meshgrid(x, y)
+    Z = np.sin(np.sqrt(X**2 + Y**2))
+    ax.contourf(X, Y, Z, cmap="YlOrRd")
+    st.pyplot(fig)
+
+def streamplot():
+    fig, ax = plt.subplots()
+    ax.set_title("Top Health Care Treatments")
+    x, y = np.linspace(-3, 3, 100), np.linspace(-3, 3, 100)
+    X, Y = np.meshgrid(x, y)
+    U = -1 - X**2 + Y
+    V = 1 + X - Y**2
+    ax.streamplot(X, Y, U, V, density=[0.5, 1], cmap="YlOrRd")
+    st.pyplot(fig)
+
+def hexbin():
+    fig, ax = plt.subplots()
+    ax.set_title("Top Health Care Treatments")
+    x = np.random.normal(0, 1, 1000)
+    y = np.random.normal(0, 1, 1000)
+    ax.hexbin(x, y, gridsize=20, cmap="YlOrRd")
+    st.pyplot(fig)
+
+def eventplot():
+    fig, ax = plt.subplots()
+    ax.set_title("Top Health Care Treatments")
+    data = np.random.rand(10, 10) > 0.5
+    ax.eventplot(np.where(data))
+    st.pyplot(fig)
+
+def tricontour():
+    fig, ax = plt.subplots()
+    ax.set_title("Top Health Care Treatments")
+    x = np.random.rand(10)
+    y = np.random.rand(10)
+    z = np.random.rand(10)
+    ax.tricontour(x, y, z, cmap="YlOrRd")
+    st.pyplot(fig)
+
+def triplot():
+    fig, ax = plt.subplots()
+    ax.set_title("Top Health Care Treatments")
+    x = np.random.rand(10)
+    y = np.random.rand(10)
+    tri = np.random.randint(0, 10, (10, 3))
+    ax.triplot(x, y, tri)
+    st.pyplot(fig)
+
+def voxel():
+    fig = plt.figure()
+    ax = fig.gca(projection='3d')
+    ax.set_title("Top Health Care Treatments")
+    x, y, z = np.indices((8, 8, 8))
+    voxels = (x < 4) & (y < 4) & (z < 4)
+    ax.voxels(voxels, facecolors='YlOrRd', edgecolor='k')
+    st.pyplot(fig)
+
+st.title("Top Health Care Treatments Visualizations")
+
+if plot_type == "Heatmap":
+    heatmap()
+elif plot_type == "3D Heatmap":
+    heatmap_3d()
+elif plot_type == "Contour":
+    contour()
+elif plot_type == "Quiver":
+    quiver()
+elif plot_type == "Contourf":
+    contourf()
+elif plot_type == "Streamplot":
+    streamplot()
+elif plot_type == "Hexbin":
+    hexbin()
+elif plot_type == "Eventplot":
+    eventplot()
+elif plot_type == "Tricontour":
+    tricontour()
+elif plot_type == "Triplot":
+    triplot()
+elif plot_type == "3D Voxel":
+    voxel()