| import gradio as gr | |
| import skops.io as sio | |
| pipe = sio.load("./Model/drug_pipeline.skops", trusted=True) | |
| def predict_drug(age, sex, blood_pressure, cholesterol, na_to_k_ratio): | |
| """Predict drug based on patient features. | |
| Args: | |
| age (int): Age of patient | |
| sex (int): Sex of patient (0 for female, 1 for male) | |
| blood_pressure (int): Blood pressure level | |
| cholesterol (int): Cholesterol level | |
| na_to_k_ratio (float): Ratio of sodium to potassium in blood | |
| Returns: | |
| str: Predicted drug label | |
| """ | |
| features = [age, sex, blood_pressure, cholesterol, na_to_k_ratio] | |
| predicted_drug = pipe.predict([features])[0] | |
| label = f"Predicted Drug: {predicted_drug}" | |
| return label | |
| inputs = [ | |
| gr.Slider(15, 74, step=1, label="Age"), | |
| gr.Radio(["M", "F"], label="Sex"), | |
| gr.Radio(["HIGH", "LOW", "NORMAL"], label="Blood Pressure"), | |
| gr.Radio(["HIGH", "NORMAL"], label="Cholesterol"), | |
| gr.Slider(6.2, 38.2, step=0.1, label="Na_to_K"), | |
| ] | |
| outputs = [gr.Label(num_top_classes=5)] | |
| examples = [ | |
| [30, "M", "HIGH", "NORMAL", 15.4], | |
| [35, "F", "LOW", "NORMAL", 8], | |
| [50, "M", "HIGH", "HIGH", 34], | |
| ] | |
| title = "Drug Classification" | |
| description = "Enter the details to correctly identify Drug type?" | |
| article = """<center> | |
| [](https://github.com/kingabzpro/CICD-for-Machine-Learning)[](https://huggingface.co/kingabzpro) | |
| **This app is a part of the Beginner's Guide to CI/CD for Machine Learning.** | |
| **It teaches how to automate training, evaluation, and deployment of models to Hugging Face using GitHub Actions.** | |
| [](https://www.datacamp.com/portfolio/kingabzpro) | |
| </center>""" | |
| gr.Interface( | |
| fn=predict_drug, | |
| inputs=inputs, | |
| outputs=outputs, | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| article=article, | |
| theme=gr.themes.Soft(), | |
| ).launch() | |