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| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| 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( | |
| ["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 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 (%)"), | |
| gr.Slider(minimum=0, maximum=100, step=0.01, label="O (%)"), | |
| ] | |
| outputs = [ | |
| 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 maximum absorption capacity (Qm) for the selected pharmaceutical based on various input parameters." | |
| gr.Interface(fn=predict_qm, inputs=inputs, outputs=outputs, title=title, description=description).launch() | |
| def predict_qm(pharmaceutical, temp, time, ps, bet, pv, c, h, n, o): | |
| """ | |
| Predicts maximum adsorption capacity (Qm) based on input features for the selected pharmaceutical. | |
| Parameters: | |
| 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. | |
| 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: The predicted maximum adsorption capacity (Qm). | |
| """ | |
| # Concatenate the inputs into a numpy array | |
| 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 | |
| with open('xgb_best_params.pkl', 'rb') as file: | |
| loaded_model = pickle.load(file) | |
| # Make predictions using the loaded machine learning model | |
| prediction = loaded_model.predict(input_data) | |
| qm = np.round(prediction[0], 2) | |
| return qm | |
| if __name__ == '__main__': | |
| # Create GUI | |
| create_gui() | |