Update app.py

app.py

@@ -4,7 +4,7 @@ import gradio as gr
 import matplotlib.pyplot as plt
 
 # Load the pre-trained model
-model = joblib.load("best_model.pkl")  # Ensure correct path
+model = joblib.load("best_model.pkl")  
 
 # Function to predict and generate the plot
 def predict_and_plot(*input_features):
@@ -98,7 +98,7 @@ def predict_and_plot(*input_features):
     if warnings:
         pred_text += "\n\n" + "\n".join(warnings)
 
-    # Marketing text
+    # Team text
     marketing_text = (
         "**Developed by:**  \nMuntasir Shehab*, Reza Taherdangkoo and Christoph Butscher  \n\n"
         "**Institution:**  \nTU Bergakademie Freiberg, Institute of Geotechnics  \n"
@@ -107,7 +107,7 @@ def predict_and_plot(*input_features):
 
     return pred_text, plot_path_png, plot_path_png, plot_path_eps, marketing_text
 
-# Modified input fields configuration
+# Input fields configuration
 input_fields = [
     # First two dropdowns (never show trained range)
     {"name": "Condition", "choices": ["Confined", "Unconfined"], "default": "Confined", "trained_range": "0-1", "show_trained": False},
@@ -127,7 +127,7 @@ input_fields = [
     {"name": "Specific suction to calculate the water content at single point", "min": 0.001, "max": 100000, "default": 10, "unit": "hPa", "step": 1, "trained_range": "0.01-100000", "show_trained": True},
 ]
 
-# Modified input component creation
+# Input component creation
 inputs = []
 for i, field in enumerate(input_fields):
     if i < 2:  # Condition and Path Type dropdowns