Upload app.py

app.py

@@ -0,0 +1,40 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+from statsmodels.tsa.arima.model import ARIMA
+from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
+import plotly.graph_objects as go
+
+def forecast_stock(days_ahead):
+    df = pd.read_excel("Microsoft_stock_data.xlsx")
+    df['Date'] = pd.to_datetime(df['Date'])
+    df = df.sort_values('Date')
+    
+    data = df['Close'].values
+    
+    model = ARIMA(data, order=(1,1,1))
+    fitted = model.fit()
+    
+ 
+    forecast = fitted.forecast(steps=int(days_ahead))
+    
+ 
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df['Date'].tail(100), y=data[-100:], name='Historical'))
+    future_dates = pd.date_range(start=df['Date'].iloc[-1], periods=int(days_ahead)+1)[1:]
+    fig.add_trace(go.Scatter(x=future_dates, y=forecast, name='Forecast'))
+    fig.update_layout(title='Stock Price Forecast', xaxis_title='Date', yaxis_title='Price')
+    
+    return fig
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 📈 Time Series Forecasting: ARIMA vs LSTM")
+    gr.Markdown("**Microsoft Stock Price Forecasting** - This app demonstrates ARIMA model forecasting.")
+    
+    days = gr.Slider(1, 90, value=30, label="Days to Forecast")
+    plot = gr.Plot()
+    
+    demo.load(forecast_stock, days, plot)
+    days.change(forecast_stock, days, plot)
+
+demo.launch()