Sync App files

app.py

@@ -0,0 +1,74 @@
+import gradio as gr
+import skops.io as sio
+import warnings
+from sklearn.exceptions import InconsistentVersionWarning
+
+# Suppress the version warnings
+warnings.filterwarnings("ignore", category=InconsistentVersionWarning)
+
+# Explicitly specify trusted types
+trusted_types = [
+    "sklearn.pipeline.Pipeline",
+    "sklearn.preprocessing.OneHotEncoder",
+    "sklearn.preprocessing.StandardScaler",
+    "sklearn.compose.ColumnTransformer",
+    "sklearn.preprocessing.OrdinalEncoder",
+    "sklearn.impute.SimpleImputer",
+    "sklearn.tree.DecisionTreeClassifier",
+    "sklearn.ensemble.RandomForestClassifier",
+    "numpy.dtype",
+]
+pipe = sio.load("./model/drug_pipeline.skops", trusted=trusted_types)
+
+
+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 = "A Beginners 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()

requirements.txt

@@ -1,2 +1,3 @@
 scikit-learn
-skops
+skops
+gradio