housekeeping

App/README.md

@@ -1,11 +1,11 @@
 ---
 title: Drug Classification
-emoji: 🐢
-colorFrom: green
-colorTo: purple
+emoji: 💊
+colorFrom: yellow
+colorTo: red
 sdk: gradio
-sdk_version: 4.40.0
-app_file: app.py
+sdk_version: 4.16.0
+app_file: drug_app.py
 pinned: false
 license: apache-2.0
 ---

App/drug_app.py

@@ -0,0 +1,57 @@
+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 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()

App/requirements.txt

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

README.md

@@ -1,11 +1,11 @@
 ---
 title: Drug Classification
-emoji: 🐢
-colorFrom: green
-colorTo: purple
+emoji: 💊
+colorFrom: yellow
+colorTo: red
 sdk: gradio
-sdk_version: 4.40.0
-app_file: App/drug_app.py
+sdk_version: 4.16.0
+app_file: drug_app.py
 pinned: false
 license: apache-2.0
 ---

drug_app.py

@@ -1,57 +0,0 @@
-import gradio as gr
-import skops.io as sio
-
-pipe = sio.load("Model/drug_pipeline.skops", trusted=sio.get_untrusted_types(file = "Model/drug_pipeline.skops"))
-
-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()
-

requirements.txt

@@ -1,5 +0,0 @@
-scikit-learn
-skops
-black
-pandas
-matplotlib