Update app.py

app.py

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