Update app.py

Replaced matplotlib with plotly for better visualisation

app.py

@@ -1,6 +1,6 @@
+import plotly.graph_objects as go
 import streamlit as st
 import yfinance as yf
-import matplotlib.pyplot as plt
 import pandas as pd
 import numpy as np
 from urllib.request import urlopen, Request
@@ -194,6 +194,7 @@ if uploaded_file is not None:
         # Display the sector mapping table
         st.table(pd.DataFrame(list(sector_mapping.items()), columns=['ETF', 'Sector']))
 
+
         if st.button('Submit'):
             # Get trends
             stock_trend = get_trend(ticker)
@@ -205,13 +206,11 @@ if uploaded_file is not None:
             sector_trend = pd.DataFrame(scaler.fit_transform(sector_trend.values.reshape(-1,1)), index=sector_trend.index, columns=['Close'])
 
             # Plot trends
-            plt.figure(figsize=(12,6))
-            plt.plot(stock_trend.index, stock_trend.values, label=ticker)
-            plt.plot(sector_trend.index, sector_trend.values, label=sector)
-            plt.legend()
-            plt.grid(True)
-            plt.title('Stock and Sector Trend over the past 30 days')
-            st.pyplot(plt)
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=stock_trend.index, y=stock_trend['Close'], mode='lines', name=ticker))
+            fig.add_trace(go.Scatter(x=sector_trend.index, y=sector_trend['Close'], mode='lines', name=sector))
+            fig.update_layout(title='Stock and Sector Trend over the past 30 days', xaxis_title='Date', yaxis_title='Normalized Close Price', autosize=False, width=2000, height=1200)
+            st.plotly_chart(fig)
 
             st.subheader("Hourly and Daily Sentiment of {} Stock".format(ticker))
             news_table = get_news(ticker)
@@ -248,6 +247,4 @@ footer {visibility: hidden;}
 """
 st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
 
-
-
-# streamlit run "c:/Dropbox/BIMWERX/_work/YouTube/elephant/230701-10 - ML Predict/src/trend_sentiment.py"
+# streamlit run "c:/Dropbox/BIMWERX/_work/YouTube/elephant/230701-10 - ML Predict/src/stock_sentiment2.py"