Create app.py

app.py

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+import yfinance as yf
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+import gradio as gr
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler
+
+# Function to get stock data
+def fetch_stock_data(ticker, start_date, end_date):
+    stock_data = yf.download(ticker, start=start_date, end=end_date)
+    return stock_data
+
+# Prepare the dataset for LSTM
+def prepare_data(data):
+    # Scaling data
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1,1))
+
+    # Create training and testing sets (80% train, 20% test)
+    train_size = int(len(scaled_data) * 0.8)
+    train_data, test_data = scaled_data[:train_size], scaled_data[train_size:]
+
+    # Create sequences for LSTM (last 60 days used to predict the next one)
+    def create_sequences(data, time_step=60):
+        X, y = [], []
+        for i in range(len(data) - time_step - 1):
+            X.append(data[i:i + time_step])
+            y.append(data[i + time_step])
+        return np.array(X), np.array(y)
+
+    X_train, y_train = create_sequences(train_data)
+    X_test, y_test = create_sequences(test_data)
+
+    return X_train, y_train, X_test, y_test, scaler
+
+# Building the LSTM model
+def build_lstm_model(input_shape):
+    model = tf.keras.Sequential([
+        tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(input_shape[1], 1)),
+        tf.keras.layers.LSTM(50, return_sequences=False),
+        tf.keras.layers.Dense(25),
+        tf.keras.layers.Dense(1)
+    ])
+
+    model.compile(optimizer='adam', loss='mean_squared_error')
+    return model
+
+# Training the model
+def train_model(X_train, y_train):
+    model = build_lstm_model(X_train.shape)
+    model.fit(X_train, y_train, batch_size=1, epochs=1)
+    return model
+
+# Make predictions
+def make_predictions(model, X_test, scaler):
+    predictions = model.predict(X_test)
+    return scaler.inverse_transform(predictions)
+
+# Calculate metrics for buy/sell decisions
+def calculate_metrics(data, predictions):
+    start_value = data['Close'][0]
+    end_value = predictions[-1][0]
+    percentage_change = ((end_value - start_value) / start_value) * 100
+
+    highest_value = data['Close'].max()
+    lowest_value = data['Close'].min()
+
+    decision = "Buy" if end_value > start_value else "Sell"
+
+    return percentage_change, highest_value, lowest_value, decision
+
+# Plot historical vs predicted
+def plot_graph(data, predictions, ticker):
+    plt.figure(figsize=(12,6))
+    plt.plot(data.index, data['Close'], label="Historical")
+    plt.plot(data.index[len(data) - len(predictions):], predictions, label="Predicted")
+    plt.title(f'{ticker} Stock Price Prediction')
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.legend()
+    plt.grid(True)
+    plt.show()
+
+# Gradio app function
+def stock_predictor(ticker, start_date, end_date):
+    stock_data = fetch_stock_data(ticker, start_date, end_date)
+    
+    if len(stock_data) == 0:
+        return "Error: No data found. Please select a valid date range."
+    
+    X_train, y_train, X_test, y_test, scaler = prepare_data(stock_data)
+    
+    model = train_model(X_train, y_train)
+    
+    predictions = make_predictions(model, X_test, scaler)
+    
+    percentage_change, highest_value, lowest_value, decision = calculate_metrics(stock_data, predictions)
+    
+    plot_graph(stock_data, predictions, ticker)
+    
+    return {
+        "Percentage Change": percentage_change,
+        "Highest Value": highest_value,
+        "Lowest Value": lowest_value,
+        "Prediction (Buy/Sell)": decision
+    }
+
+# Define stock tickers for dropdown
+tickers = ['AAPL', 'GOOGL', 'AMZN', 'MSFT', 'TSLA', 'NFLX', 'NVDA', 'META', 'BABA', 'INTC']
+
+# Gradio interface
+gr_interface = gr.Interface(
+    fn=stock_predictor,
+    inputs=[
+        gr.Dropdown(choices=tickers, label="Select Stock Ticker"),
+        gr.Date(label="Start Date"),
+        gr.Date(label="End Date")
+    ],
+    outputs=[
+        gr.Label(label="Percentage Change"),
+        gr.Label(label="Highest Value"),
+        gr.Label(label="Lowest Value"),
+        gr.Label(label="Prediction (Buy/Sell)"),
+        gr.Plot(label="Stock Performance")
+    ],
+    title="Stock Prediction App"
+)
+
+gr_interface.launch()