Update app.py

app.py

@@ -9,9 +9,70 @@ st.markdown("""
 4. Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It is designed to be easy to use and supports both convolutional and recurrent neural networks.
 5. MXNet: MXNet is a deep learning framework that is known for its speed and scalability. It supports multiple programming languages and is used by several large companies for machine learning tasks.
 
-
+# Pytorch demo with knowledge trees
 """)
 
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+import torch
+def plot_knowledge_trees(knowledge_trees):
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection='3d')
+    
+    xs = torch.arange(len(knowledge_trees))
+    ys = torch.arange(len(knowledge_trees[0]))
+    xs, ys = torch.meshgrid(xs, ys)
+    
+    zs = []
+    for i in range(len(knowledge_trees)):
+        zs.append([kt[i] for kt in knowledge_trees])
+    
+    zs = torch.tensor(zs)
+    ax.plot_surface(xs.numpy(), ys.numpy(), zs.numpy(), cmap='coolwarm')
+    
+    ax.set_xlabel('States')
+    ax.set_ylabel('Knowledge Trees')
+    ax.set_zlabel('Number of Nodes')
+    
+    return fig
+
+def plot_population(states, populations):
+    fig, ax = plt.subplots()
+    ax.bar(states, populations)
+    
+    ax.set_xlabel('States')
+    ax.set_ylabel('Population')
+    
+    return fig
+
+
+def main():
+    st.title('State Populations and Knowledge Trees')
+    
+    # Define the state populations
+    states = ['California', 'Texas', 'Florida', 'New York']
+    populations = [39538223, 29145505, 21538187, 19849399]
+    
+    # Define the knowledge trees for each state
+    knowledge_trees = [
+        [100, 200, 300, 400],
+        [150, 250, 350, 450],
+        [120, 220, 320, 420],
+        [130, 230, 330, 430]
+    ]
+    
+    # Generate the plots
+    st.write('## State Populations')
+    fig_population = plot_population(states, populations)
+    st.pyplot(fig_population)
+    
+    st.write('## Descending Order Lists of Knowledge Trees')
+    fig_knowledge_trees = plot_knowledge_trees(knowledge_trees)
+    st.pyplot(fig_knowledge_trees)
+
+if __name__ == '__main__':
+    main()
+
 st.markdown("""
 # Health Related Examples:
 1. Health Conditions By State:  https://huggingface.co/spaces/awacke1/HealthConditionsTest
@@ -19,7 +80,6 @@ st.markdown("""
 3. Health Care and AI Datasets: https://huggingface.co/spaces/awacke1/Health-Care-AI-and-Datasets
 4. Zero Shot Classifier Facebook: https://huggingface.co/spaces/awacke1/Zero-shot-classification-facebook-bart-large-mnli
 5. Zero Shot Classifier Valhalla: https://huggingface.co/spaces/awacke1/Zero-Shot-Classification-valhalla-distilbart-mnli-12-1
-6. 
 
 """)