Sync App files

README.md

@@ -1,13 +1,11 @@
 ---
 title: Drug Classification
-emoji: 👁
-colorFrom: gray
-colorTo: yellow
+emoji: 💊
+colorFrom: yellow
+colorTo: red
 sdk: gradio
-sdk_version: 5.29.1
-app_file: app.py
+sdk_version: 4.16.0
+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
+---

drug_app.py

@@ -0,0 +1,64 @@
+import gradio as gr
+import skops.io as sio
+
+# Get untrusted types from the file (for older skops versions)
+untrusted_types = sio.get_untrusted_types(file="./Model/drug_pipeline.skops")
+print("Untrusted types found:", untrusted_types)  # Review this output
+
+# If you trust these types, pass them to the loader
+pipe = sio.load("./Model/drug_pipeline.skops", trusted=untrusted_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. 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 +1,3 @@
+scikit-learn
+skops
+gradio