Update app.py

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 from datetime import date
 import yfinance as yf
+import pandas as pd  # Importing pandas to avoid NameError
 from prophet import Prophet
 from prophet.plot import plot_plotly
 from plotly import graph_objs as go
@@ -33,7 +34,7 @@ data = load_data(selected_asset)
 data_load_state.text('Loading data... done!')
 
 # Display raw data
-st.subheader('Raw data')
+st.subheader('Raw Data')
 st.write(data.tail())
 
 # Ensure 'Close' prices are numeric and handle any missing values
@@ -43,8 +44,8 @@ data.dropna(subset=['Close'], inplace=True)
 # Plot raw data function
 def plot_raw_data():
     fig = go.Figure()
-    fig.add_trace(go.Scatter(x=data['Date'], y=data['Open'], name="Open Price"))
-    fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name="Close Price"))
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['Open'], name="Open Price", line=dict(color='blue')))
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name="Close Price", line=dict(color='orange')))
     fig.layout.update(title_text='Time Series Data with Rangeslider', xaxis_rangeslider_visible=True)
     st.plotly_chart(fig)
 
@@ -64,14 +65,30 @@ future = m.make_future_dataframe(periods=period)
 forecast = m.predict(future)
 
 # Show forecast data and plot forecast
-st.subheader('Forecast data')
+st.subheader('Forecast Data')
 st.write(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
-st.write(f'Forecast plot for {n_years} years')
+st.write(f'Forecast plot for the next {n_years} years')
 
 fig1 = plot_plotly(m, forecast)
 st.plotly_chart(fig1)
 
 # Show forecast components
-st.write("Forecast components")
+st.subheader("Forecast Components")
 fig2 = m.plot_components(forecast)
-st.write(fig2)
+st.plotly_chart(fig2)
+
+# Additional Insights: Displaying key metrics
+st.subheader("Key Metrics")
+latest_data = forecast.iloc[-1]
+st.write(f"Predicted Price: ${latest_data['yhat']:.2f}")
+st.write(f"Lower Bound: ${latest_data['yhat_lower']:.2f}")
+st.write(f"Upper Bound: ${latest_data['yhat_upper']:.2f}")
+
+# User Guidance Section
+st.sidebar.header("User Guidance")
+st.sidebar.write("""
+This application allows you to predict stock prices or cryptocurrency values based on historical data.
+- Select a stock or cryptocurrency from the dropdown menu.
+- Use the slider to choose how many years into the future you want to predict.
+- View the forecasted prices along with confidence intervals.
+""")