Update app.py

app.py

@@ -1,91 +1,89 @@
-# Import necessary libraries
 import gradio as gr
-import yfinance as yf
 import pandas as pd
-import matplotlib.pyplot as plt
-from neuralprophet import NeuralProphet
-from datetime import datetime
+import numpy as np
+from prophet import Prophet  # Using updated Prophet library
+import yfinance as yf  # Ensure yfinance is installed
+import datetime
 
-# Function to fetch historical stock data
+# Function to fetch stock data
 def get_stock_data(ticker, start_date, end_date):
     stock_data = yf.download(ticker, start=start_date, end=end_date)
-    stock_data.reset_index(inplace=True)  # Reset index to use dates properly
+    stock_data.reset_index(inplace=True)
     return stock_data
 
-# Function to preprocess data for NeuralProphet model
-def prepare_data_for_neuralprophet(stock_data):
+# Function to prepare data for Prophet model
+def prepare_data(stock_data):
     df = stock_data[['Date', 'Close']].rename(columns={'Date': 'ds', 'Close': 'y'})
     return df
 
-# Function to train NeuralProphet model and make predictions
-def predict_stock(stock_data, period):
-    df = prepare_data_for_neuralprophet(stock_data)
-    model = NeuralProphet()  # Initialize NeuralProphet model
-    model.fit(df)  # Fit the model with the historical stock data
-    
-    # Make future predictions
-    future = model.make_future_dataframe(df, periods=period)  # Create a future dataframe for predictions
-    forecast = model.predict(future)  # Predict future stock prices
-    return forecast[['ds', 'yhat1']]
-
-# Function to get buy/sell recommendation based on percentage change
-def get_recommendation(stock_data):
-    change_percent = ((stock_data['Close'].iloc[-1] - stock_data['Close'].iloc[0]) / stock_data['Close'].iloc[0]) * 100
-    if change_percent > 0:
-        return "Buy"
-    else:
-        return "Sell"
-
-# Function to plot stock data
-def plot_stock(stock_data, forecast):
-    plt.figure(figsize=(10, 5))
-    plt.plot(stock_data['Date'], stock_data['Close'], label='Historical Closing Price')
-    plt.plot(forecast['ds'], forecast['yhat1'], label='Predicted Closing Price')
-    plt.xlabel("Date")
-    plt.ylabel("Stock Price")
-    plt.title("Stock Price Prediction")
-    plt.legend()
-    plt.grid(True)
-    plt.savefig("stock_prediction_plot.png")  # Save the plot as an image
-    plt.close()
-    return "stock_prediction_plot.png"
-
-# Main function to handle user inputs and return results
-def stock_prediction_app(ticker, start_date, end_date, prediction_period):
-    stock_data = get_stock_data(ticker, start_date, end_date)  # Fetch historical stock data
-    forecast = predict_stock(stock_data, prediction_period)  # Predict future prices
-    recommendation = get_recommendation(stock_data)  # Get buy/sell recommendation
-    plot_file = plot_stock(stock_data, forecast)  # Plot stock data and predictions
+# Function to make predictions using Prophet
+def make_predictions(df, periods=90):
+    model = Prophet()
+    model.fit(df)
+    future = model.make_future_dataframe(periods=periods)
+    forecast = model.predict(future)
+    return forecast
 
-    # Get the highest and lowest closing prices in the historical data
-    high = stock_data['Close'].max()
-    low = stock_data['Close'].min()
-    percentage_change = ((stock_data['Close'].iloc[-1] - stock_data['Close'].iloc[0]) / stock_data['Close'].iloc[0]) * 100
+# Function to calculate percentage change
+def calculate_percentage_change(stock_data):
+    start_value = stock_data['Close'].iloc[0]
+    end_value = stock_data['Close'].iloc[-1]
+    percentage_change = ((end_value - start_value) / start_value) * 100
+    return percentage_change
 
-    return high, low, percentage_change, recommendation, plot_file
+# Function to get stock statistics (high/low)
+def get_stock_stats(stock_data):
+    high_value = stock_data['Close'].max()
+    low_value = stock_data['Close'].min()
+    return high_value, low_value
 
-# Define the stock tickers for the dropdown
-tickers = ['AAPL', 'GOOGL', 'AMZN', 'MSFT', 'TSLA', 'NFLX', 'NVDA', 'INTC', 'AMD', 'FB']
+# Main function for UI
+def stock_prediction(ticker, start_date, end_date):
+    stock_data = get_stock_data(ticker, start_date, end_date)
+    
+    # Get statistics
+    percentage_change = calculate_percentage_change(stock_data)
+    high_value, low_value = get_stock_stats(stock_data)
+    
+    # Prepare data for prediction
+    df = prepare_data(stock_data)
+    forecast = make_predictions(df)
+    
+    # Generate buy/sell suggestion (simple rule-based)
+    suggestion = "Buy" if percentage_change < 0 else "Sell"
+    
+    # Historical stock performance and predicted future trend
+    historical_chart = stock_data[['Date', 'Close']]
+    predicted_chart = forecast[['ds', 'yhat']]
+    
+    return historical_chart, predicted_chart, percentage_change, high_value, low_value, suggestion
 
-# Create the Gradio interface using the latest Gradio API
-app = gr.Interface(
-    fn=stock_prediction_app,
-    inputs=[
-        gr.Dropdown(choices=tickers, label="Stock Ticker"),
-        gr.Textbox(label="Start Date (YYYY-MM-DD)"),
-        gr.Textbox(label="End Date (YYYY-MM-DD)"),
-        gr.Slider(1, 365, label="Prediction Period (Days)")
-    ],
-    outputs=[
-        gr.Textbox(label="Highest Value"),
-        gr.Textbox(label="Lowest Value"),
-        gr.Textbox(label="Percentage Change"),
-        gr.Textbox(label="Buy/Sell Recommendation"),
-        gr.Image(type="filepath", label="Stock Performance and Prediction Graph")
-    ],
-    title="AI-Powered Stock Prediction App",
-    description="Predict future stock prices, calculate highest and lowest prices, percentage change, and get a buy/sell recommendation based on historical data."
-)
+# UI setup
+with gr.Blocks() as app:
+    gr.Markdown("# Stock Prediction App")
+    
+    stock_ticker = gr.Dropdown(
+        label="Select Stock Ticker", 
+        choices=["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA", "NFLX", "FB", "NVDA", "IBM", "ORCL"]
+    )
+    
+    start_date = gr.DatePicker(label="Start Date", value=datetime.date(2023, 1, 1))
+    end_date = gr.DatePicker(label="End Date", value=datetime.date.today())
+    
+    historical_chart_output = gr.LinePlot(label="Historical Stock Performance")
+    predicted_chart_output = gr.LinePlot(label="Predicted Future Stock Performance")
+    percentage_change_output = gr.Textbox(label="Percentage Change")
+    high_value_output = gr.Textbox(label="Highest Stock Value")
+    low_value_output = gr.Textbox(label="Lowest Stock Value")
+    suggestion_output = gr.Textbox(label="Buy/Sell Suggestion")
+    
+    # Button to make predictions
+    predict_button = gr.Button("Predict")
+    
+    predict_button.click(
+        stock_prediction, 
+        inputs=[stock_ticker, start_date, end_date], 
+        outputs=[historical_chart_output, predicted_chart_output, percentage_change_output, high_value_output, low_value_output, suggestion_output]
+    )
 
-# Launch the Gradio app
-app.launch()
+app.launch()