Add application file

app.py

@@ -0,0 +1,78 @@
+import gradio as gr
+import yfinance as yf
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from datetime import datetime, timedelta
+import matplotlib.pyplot as plt
+
+def get_stock_data(ticker):
+    today = datetime.today().strftime('%Y-%m-%d')
+    year_ago = (datetime.today() - timedelta(days=365)).strftime('%Y-%m-%d')
+    stock_data = yf.download(ticker, start=year_ago, end=today)
+    return stock_data
+
+def preprocess_data(data):
+    data['Date'] = pd.to_datetime(data.index)
+    data['Date_ordinal'] = data['Date'].map(datetime.toordinal)
+    return data[['Date_ordinal', 'Close']]
+
+def train_model(data):
+    X = data[['Date_ordinal']]
+    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, date):
+    date_ordinal = datetime.toordinal(pd.to_datetime(date))
+    date_df = pd.DataFrame([[date_ordinal]], columns=['Date_ordinal'])
+    prediction = model.predict(date_df)
+    return prediction[0]
+
+def plot_prediction(stock_data, ticker, prediction_date, predicted_price):
+    plt.figure(figsize=(12, 6))
+    plt.plot(stock_data.index, stock_data['Close'], label='Historical Data')
+    plt.scatter(prediction_date, predicted_price, color='red', label='Prediction')
+    plt.title(f'{ticker} Stock Price Prediction')
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.legend()
+    plt.grid(True)
+    plt.savefig('prediction_plot.png')
+    return 'prediction_plot.png'
+
+def predict_stock(ticker, date):
+    stock_data = get_stock_data(ticker)
+    
+    if stock_data.empty:
+        return "No data found for the given ticker.", None
+    
+    latest_price = stock_data['Close'].iloc[-1]
+    
+    processed_data = preprocess_data(stock_data)
+    model = train_model(processed_data)
+    
+    try:
+        predicted_price = predict_price(model, date)
+        plot_path = plot_prediction(stock_data, ticker, pd.to_datetime(date), predicted_price)
+        return f"The predicted closing price for {ticker} on {date} is: ${predicted_price:.2f}", plot_path
+    except ValueError:
+        return "Invalid date format. Please enter the date in YYYY-MM-DD format.", None
+
+# Gradio app interface
+
+inputs = [
+    gr.Textbox(label="Enter the stock ticker"),
+    gr.Textbox(label="Enter the date (YYYY-MM-DD) for the prediction")
+]
+outputs = [
+    gr.Text(label="Prediction"),
+    gr.Image(label="Prediction Plot")
+]
+
+gr.Interface(fn=predict_stock, inputs=inputs, outputs=outputs, title="Stock Price Prediction").launch(share=True)