#1. Importing Lib

import gradio as gr
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

#2. Data Preprocessing

df=pd.read_csv("WineQT.csv")

x=df.drop(["Id","quality"],axis=1)
y = df["quality"]

df["quality"].unique()

#3. Modeling Part

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)
model=RandomForestClassifier()

model.fit(x_test,y_test)
model.fit(x_train,y_train)

x_predict=model.predict(x_train)
x_accuracy=accuracy_score(x_predict,y_train)

y_predict=model.predict(x_test)
y_accuracy=accuracy_score(y_predict,y_test)

#4. UI For Model

# Function to make predictions
def predict_wine_quality(fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol):
    input_data = np.array([[fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol]])
    prediction = model.predict(input_data)

    return f"Predicted Wine Quality: {prediction[0]}"

# Create the Gradio interface
iface = gr.Interface(
    fn=predict_wine_quality,  # Function that makes predictions
    inputs=[
        gr.Slider(minimum=0.0, maximum=15.0, step=0.1, label="Fixed Acidity"),
        gr.Slider(minimum=0.0, maximum=2.0, step=0.1, label="Volatile Acidity"),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label="Citric Acid"),
        gr.Slider(minimum=0.0, maximum=20.0, step=0.1, label="Residual Sugar"),
        gr.Slider(minimum=0.0, maximum=0.2, step=0.01, label="Chlorides"),
        gr.Slider(minimum=0.0, maximum=100.0, step=1, label="Free Sulfur Dioxide"),
        gr.Slider(minimum=0.0, maximum=300.0, step=1, label="Total Sulfur Dioxide"),
        gr.Slider(minimum=0.990, maximum=1.100, step=0.001, label="Density"),
        gr.Slider(minimum=2.5, maximum=4.0, step=0.1, label="pH"),
        gr.Slider(minimum=0.3, maximum=2.0, step=0.1, label="Sulphates"),
        gr.Slider(minimum=8.0, maximum=15.0, step=0.1, label="Alcohol")
    ],  # Input fields for the features of the wine
    outputs="text"  # Output the predicted wine quality as text
)

# Launch the Gradio UI
iface.launch()