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import gradio as gr
import pandas as pd
import joblib
import os
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
MODEL_PATH = "rf_model.pkl"
DATA_URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
# ---------------------------
# TRAIN MODEL (only if needed)
# ---------------------------
def train_model():
print("Downloading white wine dataset...")
df = pd.read_csv(DATA_URL, sep=';')
feature_names = [
'fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',
'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',
'pH', 'sulphates', 'alcohol'
]
X = df[feature_names]
y = df['quality']
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
print("Training Random Forest model...")
model = RandomForestClassifier(
n_estimators=300,
max_depth=12,
random_state=42
)
model.fit(X_train, y_train)
joblib.dump(model, MODEL_PATH)
print("Model saved as rf_model.pkl")
return model
# Load or train model
if os.path.exists(MODEL_PATH):
print("Loading existing model...")
model = joblib.load(MODEL_PATH)
else:
model = train_model()
# ---------------------------
# PREDICTION FUNCTION
# ---------------------------
feature_names = [
'fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',
'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',
'pH', 'sulphates', 'alcohol'
]
def predict_quality(*inputs):
df = pd.DataFrame([inputs], columns=feature_names)
prediction = model.predict(df)[0]
return f"Predicted Wine Quality: {prediction}"
# ---------------------------
# GRADIO UI
# ---------------------------
inputs_ui = [gr.Number(label=name) for name in feature_names]
demo = gr.Interface(
fn=predict_quality,
inputs=inputs_ui,
outputs=gr.Textbox(label="Prediction"),
title="🍾 White Wine Quality Predictor (Trains on HF Space)",
description="Random Forest model trained on the UCI White Wine Quality dataset."
)
demo.launch()
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