Update app.py
Browse files
app.py
CHANGED
@@ -1,27 +1,16 @@
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import
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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import gradio as gr
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print(f"Pandas version: {pd.__version__}")
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print(f"Numpy version: {np.__version__}")
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print(f"TensorFlow version: {tf.__version__}")
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print(f"Gradio version: {gr.__version__}")
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import yfinance as yf
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import pandas as pd
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def fetch_data(ticker, start_date, end_date):
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# Fetch historical data for the given ticker symbol
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data = yf.download(ticker, start=start_date, end=end_date)
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return data
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# Example usage
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# data = fetch_data("AAPL", "2023-01-01", "2023-10-01")
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from sklearn.preprocessing import MinMaxScaler
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import numpy as np
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def prepare_data(data):
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# Preprocessing
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scaler = MinMaxScaler(feature_range=(0, 1))
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@@ -43,10 +32,6 @@ def create_model(input_shape):
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model.compile(optimizer='adam', loss='mean_squared_error')
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return model
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# Example usage
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# x_train, y_train, scaler = prepare_data(data)
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# model = create_model((x_train.shape[1], 1))
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# model.fit(x_train, y_train, batch_size=1, epochs=1)
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def predict_next_days(model, last_60_days, scaler):
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last_60_days = np.array(last_60_days).reshape(-1, 1)
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last_60_days_scaled = scaler.transform(last_60_days)
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@@ -58,6 +43,17 @@ def predict_next_days(model, last_60_days, scaler):
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predicted_price = model.predict(X_test)
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predicted_price = scaler.inverse_transform(predicted_price) # Reverse scaling
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return predicted_price[0][0]
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def stock_prediction(ticker, start_date, end_date):
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data = fetch_data(ticker, start_date, end_date)
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x_train, y_train, scaler = prepare_data(data)
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@@ -73,6 +69,8 @@ def stock_prediction(ticker, start_date, end_date):
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highest_value = data['Close'].max()
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lowest_value = data['Close'].min()
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return {
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"Predicted Price": predicted_price,
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"Percentage Change": percentage_change,
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@@ -92,16 +90,3 @@ gr.Interface(
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],
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outputs=["json"],
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).launch()
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import matplotlib.pyplot as plt
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def plot_graph(data, predicted_prices):
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plt.figure(figsize=(14, 5))
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plt.plot(data['Close'], label='Historical Prices', color='blue')
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plt.plot(predicted_prices, label='Predicted Prices', color='red')
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plt.title('Stock Price Prediction')
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plt.xlabel('Date')
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plt.ylabel('Stock Price')
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plt.legend()
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plt.show()
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# Call this function in your `stock_prediction` function to plot the graph
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import yfinance as yf
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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import gradio as gr
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import matplotlib.pyplot as plt
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from sklearn.preprocessing import MinMaxScaler
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def fetch_data(ticker, start_date, end_date):
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# Fetch historical data for the given ticker symbol
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data = yf.download(ticker, start=start_date, end=end_date)
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return data
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def prepare_data(data):
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# Preprocessing
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scaler = MinMaxScaler(feature_range=(0, 1))
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model.compile(optimizer='adam', loss='mean_squared_error')
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return model
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def predict_next_days(model, last_60_days, scaler):
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last_60_days = np.array(last_60_days).reshape(-1, 1)
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last_60_days_scaled = scaler.transform(last_60_days)
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predicted_price = model.predict(X_test)
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predicted_price = scaler.inverse_transform(predicted_price) # Reverse scaling
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return predicted_price[0][0]
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def plot_graph(data, predicted_prices):
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plt.figure(figsize=(14, 5))
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plt.plot(data['Close'], label='Historical Prices', color='blue')
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plt.plot(predicted_prices, label='Predicted Prices', color='red')
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plt.title('Stock Price Prediction')
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plt.xlabel('Date')
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plt.ylabel('Stock Price')
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plt.legend()
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plt.show()
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def stock_prediction(ticker, start_date, end_date):
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data = fetch_data(ticker, start_date, end_date)
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x_train, y_train, scaler = prepare_data(data)
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highest_value = data['Close'].max()
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lowest_value = data['Close'].min()
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plot_graph(data, predicted_price)
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return {
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"Predicted Price": predicted_price,
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"Percentage Change": percentage_change,
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],
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outputs=["json"],
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).launch()
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