Update app.py

app.py

@@ -1,32 +1,30 @@
-import random
-from itertools import count
-import plotly.express as px
-import pandas as pd
-import matplotlib.pyplot as plt
-from matplotlib.animation import FuncAnimation
 import streamlit as st
+import pandas as pd
+import plotly.express as px
+from itertools import count
+import random
+import time
 
-plt.style.use('fivethirtyeight')
-
-x_vals = []
-y_vals = []
+# Initialize count for x-axis and empty DataFrame for storing data
 index = count()
+data = pd.DataFrame(columns=['x', 'y'])
 
-def animate(i):
-    x_vals.append(next(index))
-    y_vals.append(random.randint(0, 5))
-    plt.cla()  # Clear the current axes.
-    plt.plot(x_vals, y_vals)
-
-# fig, ax = plt.subplots()  # Create a figure and an axes to plot.
-# ani = FuncAnimation(fig, animate, interval=1000)
-
-ani = FuncAnimation(plt.gcf(), animate, interval=1000)
-
+# Placeholder for the Plotly chart
+chart_placeholder = st.empty()
 
-# st.pyplot(fig)  # Use this line if integrating with Streamlit
+while True:  # This loop will simulate real-time data updates
+    # Append new data point
+    new_x = next(index)
+    new_y = random.randint(0, 5)
+    new_data = pd.DataFrame({'x': [new_x], 'y': [new_y]})
+    data = pd.concat([data, new_data], ignore_index=True)
 
-plt.tight_layout()
+    # Create a new Plotly figure
+    fig = px.line(data, x='x', y='y', title="Real-time Data Plot")
+    fig.update_layout(xaxis_title='X Axis', yaxis_title='Y Axis', autosize=True)
 
+    # Display the figure using Streamlit
+    chart_placeholder.plotly_chart(fig, use_container_width=True)
 
-st.write(plt.show())
+    # Pause for a moment before updating the chart with new data
+    time.sleep(1)