Create app.py

app.py

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+import yfinance as yf
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import gradio as gr
+
+# Define stock tickers
+stock_tickers = ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA", "FB", "NFLX", "NVDA", "BRK.B", "DIS"]
+
+def fetch_data(ticker, start_date, end_date):
+    """Fetch historical stock data."""
+    data = yf.download(ticker, start=start_date, end=end_date)
+    return data
+
+def train_model(data):
+    """Train a Linear Regression model on the stock data."""
+    data['Date'] = data.index.map(pd.Timestamp.timestamp)
+    X = data[['Date']]
+    y = data['Close']
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+
+    return model
+
+def predict_price(model, today):
+    """Predict the stock price using the trained model."""
+    prediction = model.predict(np.array([[pd.Timestamp(today).timestamp()]]))
+    return prediction[0]
+
+def stock_analysis(ticker, start_date, end_date):
+    """Perform stock analysis and predictions."""
+    # Fetch data
+    data = fetch_data(ticker, start_date, end_date)
+    
+    # Train model
+    model = train_model(data)
+    
+    # Calculate statistics
+    today = pd.Timestamp.today()
+    predicted_price = predict_price(model, today)
+    
+    percentage_change = ((data['Close'][-1] - data['Close'][0]) / data['Close'][0]) * 100
+    highest_value = data['Close'].max()
+    lowest_value = data['Close'].min()
+    
+    # Create a plot for historical and predicted performance
+    plt.figure(figsize=(12, 6))
+    plt.plot(data.index, data['Close'], label='Historical Prices', color='blue')
+    future_dates = pd.date_range(start=today, periods=90, freq='D')
+    future_prices = [predict_price(model, date) for date in future_dates]
+    plt.plot(future_dates, future_prices, label='Predicted Prices', color='orange')
+    plt.title(f"{ticker} Stock Price Prediction")
+    plt.xlabel("Date")
+    plt.ylabel("Price")
+    plt.legend()
+    plt.grid()
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    plt.savefig('stock_analysis.png')
+    
+    return (
+        predicted_price,
+        percentage_change,
+        highest_value,
+        lowest_value,
+        'Buy' if predicted_price > data['Close'][-1] else 'Sell',
+        'stock_analysis.png'
+    )
+
+# Gradio UI
+inputs = [
+    gr.inputs.Dropdown(choices=stock_tickers, label="Select Stock Ticker"),
+    gr.inputs.Date(label="Start Date"),
+    gr.inputs.Date(label="End Date"),
+]
+
+outputs = [
+    gr.outputs.Textbox(label="Predicted Price"),
+    gr.outputs.Textbox(label="Percentage Change (%)"),
+    gr.outputs.Textbox(label="Highest Price"),
+    gr.outputs.Textbox(label="Lowest Price"),
+    gr.outputs.Textbox(label="Recommendation (Buy/Sell)"),
+    gr.outputs.Image(label="Stock Price Analysis")
+]
+
+gr.Interface(fn=stock_analysis, inputs=inputs, outputs=outputs, title="Stock Price Predictor").launch()