Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+
+# Load the dataset and preprocess
+df = pd.read_csv('GOOG.csv')
+df = df.drop(columns=[
+    'symbol', 'adjClose', 'adjHigh', 'adjLow', 'adjOpen', 'adjVolume', 'divCash', 'splitFactor'
+], axis=1)
+
+# Split the data
+X = df[['open', 'high', 'low', 'volume']].values
+y = df['close'].values
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+
+# Train the model
+regressor = LinearRegression()
+regressor.fit(X_train, y_train)
+
+# Define the prediction function
+def predict_stock_price(open_price, high_price, low_price, volume):
+    input_data = np.array([[open_price, high_price, low_price, volume]])
+    predicted_price = regressor.predict(input_data)[0]
+    return round(predicted_price, 2)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_stock_price,
+    inputs=[
+        gr.Number(label="Opening Price"),
+        gr.Number(label="High Price"),
+        gr.Number(label="Low Price"),
+        gr.Number(label="Volume")
+    ],
+    outputs=gr.Text(label="Predicted Closing Price"),
+    title="Stock Price Prediction App",
+    description="Enter stock data to predict the closing price using a Linear Regression model."
+)
+
+# Run the Gradio app
+if __name__ == "__main__":
+    iface.launch()