Update app.py

app.py

@@ -1,18 +1,14 @@
 import gradio as gr
 import numpy as np
-import pandas as pd
-
-# Load dataset (for normalization values)
-df = pd.read_csv("updated_stroke_risk_dataset.csv")
-
-# Compute mean and std from dataset (for normalization)
-mu = df.drop(columns=["Stroke Risk (%)", "At Risk (Binary)", "Age"]).mean().values
-sigma = df.drop(columns=["Stroke Risk (%)", "At Risk (Binary)", "Age"]).std().values
-sigma = np.where(sigma == 0, 1, sigma)  # Prevent division by zero
 
 # Load trained model parameters (assumed to be saved as .npy file)
 theta_final = np.load("theta_final.npy")  # Save your trained theta and upload it to HF Spaces
 
+# Mean and standard deviation for normalization (precomputed from dataset)
+mu = np.array([...])  # Replace with actual values
+sigma = np.array([...])  # Replace with actual values
+sigma = np.where(sigma == 0, 1, sigma)  # Prevent division by zero
+
 def predict_stroke_risk(*symptoms):
     user_input = np.array([symptoms])
     normalized_input = (user_input - mu) / sigma
@@ -39,4 +35,4 @@ demo = gr.Interface(
     description="Check your stroke risk based on symptoms."
 )
 
-demo.launch()
+demo.launch()