Update app.py

app.py

@@ -1,9 +1,13 @@
+#1. Importing Lib
+
 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)
@@ -11,12 +15,12 @@ y = df["quality"]
 
 df["quality"].unique()
 
-x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)
+#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)
@@ -25,13 +29,7 @@ x_accuracy=accuracy_score(x_predict,y_train)
 y_predict=model.predict(x_test)
 y_accuracy=accuracy_score(y_predict,y_test)
 
-import gradio as gr
-
-# Assuming you've already trained the RandomForest model
-model = RandomForestClassifier()
-
-# Fit the model with your training data (re-run if needed)
-model.fit(x_train, y_train)
+#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):