| import gradio as gr |
| import skops.io as sio |
|
|
| pipe = sio.load("./drug_pipeline.skops", trusted=["numpy.dtype"]) |
|
|
|
|
| def predict_drug(age, sex, blood_pressure, cholesterol, na_to_k_ratio): |
| """Predict drugs based on patient features. |
| |
| Args: |
| age (int): Age of patient |
| sex (str): Sex of patient |
| blood_pressure (str): Blood pressure level |
| cholesterol (str): 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 = "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." |
|
|
|
|
| gr.Interface( |
| fn=predict_drug, |
| inputs=inputs, |
| outputs=outputs, |
| examples=examples, |
| title=title, |
| description=description, |
| article=article, |
| theme=gr.themes.Soft(), |
| ).launch() |
|
|
|
|
| |
