Update app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+from sklearn.multioutput import MultiOutputRegressor
+from sklearn.ensemble import GradientBoostingRegressor
+import pickle
+
+def create_gui():
+    """
+    Creates and launches the Gradio user interface for the biomass adsorption prediction.
+    """
+    # Define the input and output components of the GUI
+    inputs = [
+        gr.Dropdown(choices=["Benzocaine", "Ciprofloxacin", "Citalopram", "Diclofenac", "Dimetridazole", 
+                             "Floxentine", "Ibuprofen", "Metronidazole", "Nitroimidazole", "Norfloxacin", 
+                             "Oxytetracycline", "Salicylic Acid", "Sulfadiazine", "Sulfamethazine", "Sulfamethoxazole", 
+                             "Tetracycline", "Triclosan"], 
+                    label="Pharmaceutical Type"),
+        gr.components.Slider(minimum=0, maximum=100, step=0.01, label="TemP (K)"),
+        gr.components.Slider(minimum=0, maximum=100, step=0.01, label="Time (min)"),
+        gr.components.Slider(minimum=0, maximum=100, step=0.01, label="PS (mm)"),
+        gr.components.Slider(minimum=0, maximum=100, step=0.01, label="BET (m2/g)"),
+        gr.components.Slider(minimum=0, maximum=100, step=0.01, label="PV (cm3)"),
+        gr.components.Slider(minimum=0, maximum=100, step=0.01, label="C (%)"),
+        gr.components.Slider(minimum=0, maximum=100, step=0.01, label="H (%)"),
+        gr.components.Slider(minimum=0, maximum=100, step=0.01, label="N (%)"),
+        gr.components.Slider(minimum=0, maximum=100, step=0.01, label="O (%)"),
+    ]
+
+    outputs = [
+        gr.components.Textbox(label="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."
+
+    gr.Interface(fn=biomass_prediction, inputs=inputs, outputs=outputs, title=title, description=description).launch()
+
+def biomass_prediction(*inputs):
+    """
+    Predicts properties based on input features of the biomass.
+    
+    Parameters:
+    pharm_type (str): Pharmaceutical type.
+    TemP (float): Temperature K of the biomass.
+    Time (float): Minutes for x.
+    PS (float): Pore space in mm.
+    BET (float): Area of powder in m2/g being poured into the solution.
+    PV (float): Pore volume in cm3.
+    C (float): C (%) content of the biomass.
+    H (float): H (%) content of the biomass.
+    N (float): N (%) content of the biomass.
+    O (float): O (%) content of the biomass.
+    
+    Returns:
+    float: Absorption capacity
+    """
+    # Concatenate the inputs into a numpy array
+    input_data = pd.DataFrame([inputs], 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
+
+    # Load the trained model from the pickled file
+    with open('trained_model.pkl', 'rb') as file:
+        loaded_model = pickle.load(file)
+
+    # Make predictions using the loaded machine learning model
+    prediction = loaded_model.predict(input_data)
+    prediction = np.round(prediction, 2)
+
+    # Extract prediction value and save it to a separate variable
+    output1 = prediction[0][0]
+  
+    # Return predicted output values
+    return output1
+
+if __name__ == '__main__': 
+    # Create GUI
+    create_gui()