app.py

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

def linear_regression(input_csv):
    # Load dataset from binary
    df = pd.read_csv(io.BytesIO(input_csv))

    # Assume the first column is X and the second column is Y
    if len(df.columns) < 2:
        return None, "CSV file must contain at least two columns."

    X = df.iloc[:, 0].values.reshape(-1, 1)
    y = df.iloc[:, 1].values

    # 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(df.columns[0])
    plt.ylabel(df.columns[1])
    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

# Tutorial Markdown
tutorial_markdown = """
## Tutorial

Follow these steps to use the application:

1. Prepare a CSV file with two columns. The first column should be your independent variable (X), and the second column your dependent variable (Y).
2. Upload the CSV file using the 'File Upload' field.
3. The application will automatically process the file, perform linear regression, and display the results and a plot.
"""

# Gradio interface
iface = gr.Interface(
    fn=linear_regression,
    inputs=[gr.components.File(type="binary")],
    outputs=[
        gr.components.Image(type="pil"),
        gr.components.Textbox(label="Regression Info")
    ],
    title="Automatic Linear Regression Modeling",
    description="Upload a CSV file with two columns. The first column will be used as X (independent variable) and the second as Y (dependent variable).",
    layout="vertical"
).add_instructions(tutorial_markdown)

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