Update app.py

app.py

@@ -1,14 +1,11 @@
 import gradio as gr
 import numpy as np
-import pandas as pd
 import matplotlib.pyplot as plt
-from sklearn.linear_model import LinearRegression
-from sklearn.preprocessing import MinMaxScaler
 import joblib
 
 # Load the pre-trained model and scaler
-model = joblib.load("water_quality_model.pkl")  # Ensure the model file is in the same directory
-scaler = joblib.load("scaler.pkl")
+model = joblib.load("water_quality_model.pkl")  # Ensure the model file exists
+scaler = joblib.load("scaler.pkl")  # Ensure the scaler file exists
 
 # Define water quality categories
 def classify_quality(score):
@@ -26,7 +23,7 @@ def predict_water_quality(pH, turbidity, dissolved_oxygen, nitrate_levels):
     # Prepare the input data
     data = np.array([[pH, turbidity, dissolved_oxygen, nitrate_levels]])
     scaled_data = scaler.transform(data)
-    
+
     # Predict the quality score
     score = model.predict(scaled_data)[0]
     quality = classify_quality(score)