app.py

import gradio as gr
import pandas as pd
import joblib
import requests
import os

MODEL_URL = "https://huggingface.co/munnabhaimbbsfail/linear_regression_model/resolve/main/model.pkl"
MODEL_PATH = "model.pkl"

# Download the model if not already present
if not os.path.exists(MODEL_PATH):
    response = requests.get(MODEL_URL)
    with open(MODEL_PATH, "wb") as f:
        f.write(response.content)

# Load model
model = joblib.load(MODEL_PATH)

# Define prediction function
def predict(x_values: str):
    try:
        x_list = [float(x.strip()) for x in x_values.split(",")]
        df = pd.DataFrame({"x": x_list})
        preds = model.predict(df)
        return {"predictions": preds.tolist()}
    except Exception as e:
        return {"error": str(e)}

# Create Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(label="Enter x values (comma-separated)", placeholder="e.g., 5, 10, 15"),
    outputs="json",
    title="Linear Regression Predictor",
    description="Enter a list of x values to get predictions from the trained model."
)

iface.launch()