Sync App files

README.md

@@ -1,13 +1,21 @@
 ---
 title: Drug Classification
-emoji: 🐠
+emoji: 💊
 colorFrom: yellow
 colorTo: red
 sdk: gradio
-sdk_version: 5.28.0
-app_file: app.py
+sdk_version: 5.7.1
+app_file: drug_app.py
 pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+*Learn how to automate model training, evaluation, versioning, and deployment using GitHub Actions with the easiest MLOps guide available online.*
+
+> **Follow the tutorial:** https://www.datacamp.com/tutorial/ci-cd-for-machine-learning
+
+## Project Description
+In this project, we will be using scikit-learn pipelines to train our random forest algorithm and build a drug classifier. After training, we will automate the evaluation process using CML. Finally, we will build and deploy the web application to Hugging Face Hub. 
+
+From training to evaluation, the entire process will be automated using GitHub actions. All you have to do is push the code to your GitHub repository, and within two minutes, the model will be updated on Hugging Face with the updated app, model, and results.
+

drug_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 = "This app is a part of the **[Beginner's Guide to CI/CD for Machine Learning](https://www.datacamp.com/tutorial/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

@@ -0,0 +1,2 @@
+scikit-learn
+skops