gradio_regression.py

import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestRegressor
import gradio as gr

# Load the diamonds dataset
diamonds = sns.load_dataset("diamonds")

# Prepare the features and target
X = diamonds.drop("price", axis=1)
y = diamonds["price"]

# Split the data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Define the preprocessing steps
numeric_features = ["carat", "depth", "table", "x", "y", "z"]
categorical_features = ["cut", "color", "clarity"]

preprocessor = ColumnTransformer(
    transformers=[
        ("num", StandardScaler(), numeric_features),
        ("cat", OneHotEncoder(drop="first"), categorical_features),
    ]
)


# Create a pipeline with preprocessing and model
model = Pipeline(
    [
        ("preprocessor", preprocessor),
        ("regressor", RandomForestRegressor(n_estimators=100, random_state=42)),
    ]
)

# Fit the model
model.fit(X_train, y_train)