Create app.py

app.py

@@ -0,0 +1,86 @@
+# Import necessary libraries
+import yfinance as yf
+from prophet import Prophet
+import gradio as gr
+import pandas as pd
+from datetime import datetime
+
+# Stock ticker options
+tickers = ['AAPL', 'GOOGL', 'MSFT', 'TSLA', 'AMZN', 'NFLX', 'NVDA', 'FB', 'INTC', 'AMD']
+
+# Function to fetch stock data from Yahoo Finance
+def fetch_stock_data(ticker, start_date, end_date):
+    stock_data = yf.download(ticker, start=start_date, end=end_date)
+    return stock_data
+
+# Function to train the model and predict future prices
+def predict_stock(ticker, start_date, end_date):
+    # Fetch data
+    stock_data = fetch_stock_data(ticker, start_date, end_date)
+    
+    # Prepare data for Prophet model
+    stock_df = stock_data[['Close']].reset_index()
+    stock_df = stock_df.rename(columns={'Date': 'ds', 'Close': 'y'})
+    
+    # Train Prophet model
+    model = Prophet()
+    model.fit(stock_df)
+    
+    # Make future dataframe for 3 months (90 days) prediction
+    future = model.make_future_dataframe(periods=90)
+    forecast = model.predict(future)
+    
+    # Extract key data points
+    current_price = stock_data['Close'][-1]
+    highest_price = stock_data['Close'].max()
+    lowest_price = stock_data['Close'].min()
+    percent_change = ((current_price - stock_data['Close'][0]) / stock_data['Close'][0]) * 100
+    
+    # Generate graphs
+    historical_plot = stock_data['Close'].plot(title=f"{ticker} Historical Prices").get_figure()
+    future_plot = model.plot(forecast).get_figure()
+    
+    # Determine buy/sell prediction (naive strategy: buy if current < future mean, sell otherwise)
+    future_avg = forecast['yhat'].mean()
+    buy_sell = "Buy" if current_price < future_avg else "Sell"
+    
+    # Return results
+    return {
+        "Current Price": current_price,
+        "Highest Price": highest_price,
+        "Lowest Price": lowest_price,
+        "Percent Change": percent_change,
+        "Recommendation": buy_sell,
+        "Historical Plot": historical_plot,
+        "Future Plot": future_plot
+    }
+
+# Gradio Interface
+def app_interface(ticker, start_date, end_date):
+    result = predict_stock(ticker, start_date, end_date)
+    
+    return result["Historical Plot"], result["Future Plot"], result["Current Price"], result["Highest Price"], result["Lowest Price"], result["Percent Change"], result["Recommendation"]
+
+# Gradio UI Setup
+gr_interface = gr.Interface(
+    fn=app_interface,
+    inputs=[
+        gr.Dropdown(label="Select Stock Ticker", choices=tickers, value='AAPL'),
+        gr.inputs.Date(label="Start Date", default=datetime(2020, 1, 1).strftime('%Y-%m-%d')),
+        gr.inputs.Date(label="End Date", default=datetime.now().strftime('%Y-%m-%d'))
+    ],
+    outputs=[
+        gr.Plot(label="Historical Stock Prices"),
+        gr.Plot(label="Future Stock Predictions"),
+        gr.Textbox(label="Current Price"),
+        gr.Textbox(label="Highest Price"),
+        gr.Textbox(label="Lowest Price"),
+        gr.Textbox(label="Percentage Change"),
+        gr.Textbox(label="Buy/Sell Recommendation")
+    ],
+    title="Stock Prediction App",
+    description="Select a stock, start and end date to predict future performance and get buy/sell recommendations."
+)
+
+# Launch the app
+gr_interface.launch()