app.py

import gradio as gr
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import io
from PIL import Image

def linear_regression(x_values, y_values):
    # Convert string inputs to numpy arrays
    X = np.array([float(x) for x in x_values.split(',')]).reshape(-1, 1)
    y = np.array([float(y) for y in y_values.split(',')])

    # Perform linear regression
    model = LinearRegression()
    model.fit(X, y)

    # Make predictions
    y_pred = model.predict(X)

    # Plotting
    plt.figure(figsize=(10, 6))
    plt.scatter(X, y, color='blue')
    plt.plot(X, y_pred, color='red')
    plt.xlabel("X Values")
    plt.ylabel("Y Values")
    plt.title('Linear Regression')

    # Save plot to a buffer and convert to PIL Image
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    image = Image.open(buf)

    # Regression info
    coef_info = f"Coefficient: {model.coef_[0]}\nIntercept: {model.intercept_}"

    return image, coef_info

# Gradio interface
iface = gr.Interface(
    fn=linear_regression,
    inputs=[
        gr.components.Textbox(placeholder="Enter X values separated by commas (e.g., 1,2,3)", label="X Values"),
        gr.components.Textbox(placeholder="Enter Y values separated by commas (e.g., 2,4,6)", label="Y Values")
    ],
    outputs=[
        gr.components.Image(type="pil"),
        gr.components.Textbox(label="Regression Info")
    ],
    title="Automatic Linear Regression Modeling",
    description="Enter X and Y values as comma-separated lists to perform linear regression."
)

# Launch the app
if __name__ == "__main__":
    iface.launch()