app.py

import gradio as gr
import pandas as pd
import joblib
from huggingface_hub import hf_hub_download

# Download model and feature names from Hugging Face
model_path = hf_hub_download(repo_id="alperugurcan/mercedes", filename="mercedes_model.joblib")
feature_names_path = hf_hub_download(repo_id="alperugurcan/mercedes", filename="feature_names.joblib")

# Load the saved model and feature names
model = joblib.load(model_path)
feature_names = joblib.load(feature_names_path)

def predict(*features):
    # Create a DataFrame with the input features
    df = pd.DataFrame([features], columns=feature_names)
    
    # Make prediction
    prediction = model.predict(df)[0]
    return f"Predicted time: {prediction:.2f}"

# Create the interface
inputs = [gr.Number(label=f"Feature {i+1}") for i in range(len(feature_names))]
output = gr.Textbox(label="Prediction")

interface = gr.Interface(
    fn=predict,
    inputs=inputs,
    outputs=output,
    title="Mercedes-Benz Manufacturing Time Prediction",
    description="Enter feature values to predict manufacturing time"
)

interface.launch()