Update app.py

app.py

@@ -1,56 +1,96 @@
-import gradio as gr
-import pandas as pd
-import numpy as np
 import yfinance as yf
+import numpy as np
+import pandas as pd
+import tensorflow as tf
 from sklearn.preprocessing import MinMaxScaler
-from tensorflow import keras
-
-# Load your trained model
-model = keras.models.load_model('your_model.h5')  # Ensure this path is correct
-
-# Function to predict stock prices
-def predict_stock_price(stock_ticker, start_date, end_date):
-    # Fetch data
-    data = yf.download(stock_ticker, start=start_date, end=end_date)
-    
-    # Check if data is returned
-    if data.empty:
-        return "No data available for the selected dates."
-
-    # Preprocess data
-    scaler = MinMaxScaler()
-    scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))
-
-    # Prepare input for the model
-    input_data = scaled_data[-60:]  # Use the last 60 days of data
-    input_data = input_data.reshape((1, input_data.shape[0], 1))
-
-    # Predict stock prices
-    prediction = model.predict(input_data)
-    predicted_price = scaler.inverse_transform(prediction)  # Rescale back to original price
-    
-    return f"Predicted stock price for tomorrow: ${predicted_price[0][0]:.2f}"
-
-# Create the Gradio interface
-stock_ticker_input = gr.Dropdown(
-    choices=["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA"],  # Add more tickers as needed
-    label="Select Stock Ticker"
-)
-
-start_date_input = gr.Date(label="Start Date")
-end_date_input = gr.Date(label="End Date")
-
-iface = gr.Interface(
-    fn=predict_stock_price,
-    inputs=[
-        stock_ticker_input,
-        start_date_input,
-        end_date_input
-    ],
-    outputs="text",
-    title="Stock Price Prediction App",
-    description="Enter the stock ticker and date range to predict the stock price for tomorrow."
-)
+import gradio as gr
+import matplotlib.pyplot as plt
+
+# Define stock tickers for the dropdown
+tickers = ['AAPL', 'MSFT', 'GOOGL', 'TSLA', 'AMZN', 'FB', 'NFLX', 'NVDA', 'INTC', 'IBM']
+
+# Function to fetch stock data and make predictions
+def stock_prediction_app(ticker, start_date, end_date):
+    # Fetch historical stock data from Yahoo Finance
+    stock_data = yf.download(ticker, start=start_date, end=end_date)
+    
+    # Check if data is fetched correctly
+    if stock_data.empty:
+        return "No data available for the selected date range.", None
+    
+    # Prepare the data for LSTM model
+    df_close = stock_data[['Close']]  # Use only the 'Close' column for prediction
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_data = scaler.fit_transform(df_close)
+
+    # Create datasets for training the LSTM model
+    def create_dataset(data, time_step=60):
+        X_train, y_train = [], []
+        for i in range(len(data)-time_step-1):
+            X_train.append(data[i:(i+time_step), 0])
+            y_train.append(data[i + time_step, 0])
+        return np.array(X_train), np.array(y_train)
+    
+    X_train, y_train = create_dataset(scaled_data)
+
+    # Reshape the data for LSTM [samples, time steps, features]
+    X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
+    
+    # Define LSTM model
+    lstm_model = tf.keras.Sequential([
+        tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(60, 1)),
+        tf.keras.layers.LSTM(50, return_sequences=False),
+        tf.keras.layers.Dense(25),
+        tf.keras.layers.Dense(1)
+    ])
+    
+    # Compile the model
+    lstm_model.compile(optimizer='adam', loss='mean_squared_error')
+    
+    # Train the model
+    lstm_model.fit(X_train, y_train, batch_size=1, epochs=1)
+
+    # Predict on the same data (just for demonstration)
+    predictions = lstm_model.predict(X_train)
+    predictions = scaler.inverse_transform(predictions)  # Convert back to original scale
+
+    # Create a plot to show predictions
+    plt.figure(figsize=(10, 5))
+    plt.plot(df_close.values, label='Actual Stock Price')
+    plt.plot(predictions, label='Predicted Stock Price')
+    plt.title(f'{ticker} Stock Price Prediction')
+    plt.xlabel('Days')
+    plt.ylabel('Stock Price')
+    plt.legend()
+    
+    # Save the plot to display in Gradio app
+    plt.savefig('stock_prediction_plot.png')
+
+    # Return a message and the path to the saved plot
+    return f"Prediction complete for {ticker} from {start_date} to {end_date}", 'stock_prediction_plot.png'
+
+# Create the Gradio UI for the app
+app = gr.Blocks()
+
+with app:
+    gr.Markdown("# Stock Buy/Sell Prediction App")
+    
+    # Dropdown for stock tickers
+    ticker = gr.Dropdown(tickers, label="Select Stock Ticker")
+    
+    # Textboxes for manual date input
+    start_date = gr.Textbox(label="Start Date (YYYY-MM-DD)")
+    end_date = gr.Textbox(label="End Date (YYYY-MM-DD)")
+    
+    # Button to trigger the prediction
+    predict_button = gr.Button("Predict")
+    
+    # Output fields for text and image
+    output_text = gr.Textbox(label="Prediction Result")
+    output_image = gr.Image(label="Stock Price Graph")
+    
+    # Set up button click event to run the prediction function
+    predict_button.click(fn=stock_prediction_app, inputs=[ticker, start_date, end_date], outputs=[output_text, output_image])
 
 # Launch the Gradio app
-iface.launch()
+app.launch()