Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import numpy as np
 import pandas as pd
-from sklearn.multioutput import MultiOutputRegressor
 from sklearn.ensemble import GradientBoostingRegressor
 import pickle
 
@@ -12,13 +11,13 @@ def create_gui():
     # Define the input and output components of the GUI
     inputs = [
         gr.Dropdown(
-            ["Benzocaine", "Ciprofloxacin", "Citalopram", "Diclofenac", "Dimetridazole", "Floxentine", "Ibuprofen", "Metronidazole", "Nitroimidazole", "Norfloxacin", "Oxytetracycline", "Salicylic Acid", "Sulfadiazine", "Sulfamethazine", "Sulfamethoxazole", "Tetracycline", "Triclosan"], label="Pharmaceutical Type"
+            ["Benzocaine", "Ciprofloxacin", "Citalopram", "Diclofenac", "Dimetridazole", "Floxentine", "Ibuprofen", "Metronidazole", "Nitroimidazole", "Norfloxacin", "Oxytetracycline", "Salicylic Acid", "Sulfadiazine", "Sulfamethazine", "Sulfamethoxazole", "Tetracycline", "Triclosan"], label="Select Pharmaceutical"
         ),
         gr.Slider(minimum=0, maximum=950, step=0.01, label="TemP (K)"),
         gr.Slider(minimum=0, maximum=480, step=0.01, label="Time (min)"),
         gr.Slider(minimum=0, maximum=250, step=0.01, label="PS (mm)"),
-        gr.Slider(minimum=0, maximum=2000, step=0.01, label="BET (m2/g)"),
-        gr.Slider(minimum=0, maximum=1, step=0.01, label="PV (cm3)"),
+        gr.Slider(minimum=0, maximum=2000, step=0.01, label="BET surface area (m2/g)"),
+        gr.Slider(minimum=0, maximum=1, step=0.01, label="Pore Volume (cm3)"),
         gr.Slider(minimum=0, maximum=100, step=0.01, label="C (%)"),
         gr.Slider(minimum=0, maximum=100, step=0.01, label="H (%)"),
         gr.Slider(minimum=0, maximum=100, step=0.01, label="N (%)"),
@@ -26,21 +25,21 @@ def create_gui():
     ]
 
     outputs = [
-        gr.Textbox(label="Qm (mg/g)")
+        gr.Textbox(label="Predicted Maximum Adsorption Capacity, Qm (mg/g)")
     ]
     
     # Define the title and description of the GUI
     title = "GUI for Pharmaceutical Removal via Biochar Adsorption"
-    description = "This GUI uses machine learning to predict pharmaceutical removal. The app takes ten features and predicts adsorption capacity."
+    description = "This GUI uses machine learning to predict maximum absorption capacity (Qm) for the selected pharmaceutical based on various input parameters."
 
-    gr.Interface(fn=biomass_prediction, inputs=inputs, outputs=outputs, title=title, description=description).launch()
+    gr.Interface(fn=predict_qm, inputs=inputs, outputs=outputs, title=title, description=description).launch()
 
-def biomass_prediction(pharm_type, temp, time, ps, bet, pv, c, h, n, o):
+def predict_qm(pharmaceutical, temp, time, ps, bet, pv, c, h, n, o):
     """
-    Predicts properties based on input features of the biomass.
+    Predicts maximum adsorption capacity (Qm) based on input features for the selected pharmaceutical.
     
     Parameters:
-    pharm_type (str): Pharmaceutical type.
+    pharmaceutical (str): Pharmaceutical type.
     TemP (float): Temperature K of the biomass.
     Time (float): Time it takes for biochar to remove pharmaceutical in minutes .
     PS (float): Pore space in mm.
@@ -52,32 +51,21 @@ def biomass_prediction(pharm_type, temp, time, ps, bet, pv, c, h, n, o):
     O (float): O (%) content of the biomass.
     
     Returns:
-    float: Absorption capacity
+    float: The predicted maximum adsorption capacity (Qm).
     """
 
     # Concatenate the inputs into a numpy array
-    input_data = pd.DataFrame([[pharm_type, temp, time, ps, bet, pv, c, h, n, o]],
-                              columns=['pharm type', 'TemP (K)', 'Time (min)', 'PS (mm)', 'BET (m2/g)', 'PV (cm3)',  'C (%)', 'H (%)', 'N (%)', 'O (%)'])
-
-    # Check if values are zero or negative
-    if np.all(input_data <= 0):
-        return 0
+    input_data = pd.DataFrame([[temp, time, ps, bet, pv, c, h, n, o]], columns=["Temp", "Time", "PS", "BET", "PV", "C", "H", "N", "O"])
 
     # Load the trained model from the pickled file
-    try:
-        with open('xgb_best_params.pkl', 'rb') as file:
+    with open('xgb_best_params.pkl', 'rb') as file:
             loaded_model = pickle.load(file)
-    except FileNotFoundError:
-        return "Model file not found"
+
     # Make predictions using the loaded machine learning model
     prediction = loaded_model.predict(input_data)
-    prediction = np.round(prediction, 2)
+    qm = np.round(prediction[0], 2)
 
-    # Extract prediction value and save it to a separate variable
-    output1 = prediction[0][0]
-  
-    # Return predicted output values
-    return output1
+    return qm
 
 if __name__ == '__main__': 
     # Create GUI