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app.py

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+
+import gradio as gr
+import numpy as np
+import pandas as pd
+import yfinance as yf
+from datetime import datetime
+from tensorflow.keras.models import load_model
+from joblib import load
+
+# Load the saved LSTM model and scaler
+lstm_model = load_model('/kaggle/input/madel-and-scaler/lstm_model.h5')
+scaler = load('/kaggle/input/madel-and-scaler/scaler.joblib')
+
+# Define the list of stocks
+stock_list = ['GOOG', 'AAPL', 'TSLA', 'AMZN', 'MSFT']
+
+# Function to get the last row of stock data
+def get_last_stock_data(ticker):
+    try:
+        start_date = '2010-01-01'
+        end_date = datetime.now().strftime('%Y-%m-%d')
+        data = yf.download(ticker, start=start_date, end=end_date)
+        last_row = data.iloc[-1]
+        return last_row.to_dict()
+    except Exception as e:
+        return str(e)
+
+# Function to make predictions
+def predict_stock_price(ticker, open_price, high_price, low_price, close_price, adj_close_price, volume):
+    try:
+        start_date = '2010-01-01'
+        end_date = datetime.now().strftime('%Y-%m-%d')
+        data = yf.download(ticker, start=start_date, end=end_date)
+
+        # Prepare the data
+        data = data[['Close']]
+        dataset = data.values
+        scaled_data = scaler.transform(dataset)
+
+        # Append the user inputs as the last row in the data
+        user_input = np.array([[close_price]])
+        user_input_scaled = scaler.transform(user_input)
+        scaled_data = np.vstack([scaled_data, user_input_scaled])
+
+        # Prepare the data for LSTM
+        x_test_lstm = []
+        for i in range(60, len(scaled_data)):
+            x_test_lstm.append(scaled_data[i-60:i])
+
+        x_test_lstm = np.array(x_test_lstm)
+        x_test_lstm = np.reshape(x_test_lstm, (x_test_lstm.shape[0], x_test_lstm.shape[1], 1))
+
+        # LSTM Predictions
+        lstm_predictions = lstm_model.predict(x_test_lstm)
+        lstm_predictions = scaler.inverse_transform(lstm_predictions)
+        next_day_lstm_price = lstm_predictions[-1][0]
+
+        result = f"Predicted future price for {ticker}: ${next_day_lstm_price:.2f}"
+
+        return result
+    except Exception as e:
+        return str(e)
+
+# Set up Gradio interface
+ticker_input = gr.Dropdown(choices=stock_list, label="Stock Ticker")
+
+def get_user_inputs(ticker):
+    last_data = get_last_stock_data(ticker)
+    if isinstance(last_data, str):
+        return gr.Textbox.update(value=last_data)
+    else:
+        return gr.update(inputs=[
+            gr.Number(value=last_data['Open'], label='Open'),
+            gr.Number(value=last_data['High'], label='High'),
+            gr.Number(value=last_data['Low'], label='Low'),
+            gr.Number(value=last_data['Close'], label='Close'),
+            gr.Number(value=last_data['Adj Close'], label='Adj Close'),
+            gr.Number(value=last_data['Volume'], label='Volume')
+        ])
+
+iface = gr.Interface(
+    fn=predict_stock_price,
+    inputs=[
+        ticker_input,
+        gr.Number(label="Open"),
+        gr.Number(label="High"),
+        gr.Number(label="Low"),
+        gr.Number(label="Close"),
+        gr.Number(label="Adj Close"),
+        gr.Number(label="Volume")
+    ],
+    outputs=gr.Textbox(),
+    title="Stock Price Predictor",
+    description="Select the stock ticker and input the last recorded values to predict the closing price using the LSTM model."
+)
+
+iface.launch()
+
+

lstm_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e7812e428cc87e438b827fe557e3153355a1905c22800d146dc26b976b4c9311
+size 408600

requirements.txt

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+
+gradio
+yfinance
+tensorflow
+joblib
+matplotlib
+numpy
+pandas
+scikit-learn

scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ac234b6fe95df3b478938c9c61c63f7a79419279dce91d80fbdb1a330fb90030
+size 719