Create app.py

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+# Load the trained models
+models = {
+    "Logistic": pickle.load(open("logistic_model_best.pkl", "rb")),
+    "SVC": pickle.load(open("svc_model_best.pkl", "rb")),
+    "Decision Trees": pickle.load(open("decisiontree_model_best.pkl", "rb")),
+    "Random Forest": pickle.load(open("rfc_model_best.pkl", "rb")),
+    "KNN": pickle.load(open("knn_model_best.pkl", "rb")),
+    "LDA": pickle.load(open("lda_model_best.pkl", "rb"))
+}
+
+# Define the input and output dropdowns
+age_dropdown = gr.inputs.Number(label='Age')
+sex_dropdown = gr.inputs.Dropdown(["Male", "Female"], label="Sex")
+cp_dropdown = gr.inputs.Dropdown(["1", "2", "3", "4"], label="Chest Pain Type")
+restbp_dropdown = gr.inputs.Number(label='Resting Blood pressure')
+chol_dropdown = gr.inputs.Number(label='Serum cholestoral')
+fbs_dropdown = gr.inputs.Dropdown(["0", "1"], label="Fasting Blood Sugar")
+ecg_dropdown = gr.inputs.Dropdown(["0", "1", "2"], label="Electrocardiographic Results")
+max_rt_dropdown = gr.inputs.Number(label="Maximum Heart Rate")
+exang_dropdown = gr.inputs.Dropdown(["0", "1"], label="Exercise-Induced Angina")
+oldpeak_dropdown = gr.inputs.Number(label="oldpeak =ST Depression Induced by Exercise")
+slope_dropdown = gr.inputs.Dropdown(["0", "1", "2"], label="Slope of the ST Segment")
+ca_dropdown = gr.inputs.Dropdown(["0", "1", "2", "3"], label="Number of Major Vessels")
+thal_dropdown = gr.inputs.Dropdown(["0", "1", "2"], label="Thalassemia")
+model_dropdown = gr.inputs.Dropdown(
+    ["Logistic", "SVC", "Decision Trees", "Random Forest", "KNN", "LDA"],
+    label="Model"
+)
+xoutputs = [
+    gr.outputs.Textbox(label="Probability of Heart Disease"),
+]
+# Define the function to make predictions
+import numpy as np
+
+def predict(age, sex, cp, restbp, chol, fbs, ecg, max_rt, exang, oldpeak, slope, ca, thal, model):
+    # Convert input values to their respective data types
+    age = int(age)
+    sex = 1 if sex == "Male" else 0
+    cp = int(cp)
+    restbp = int(restbp)
+    chol = int(chol)
+    fbs = int(fbs)
+    ecg = int(ecg)
+    max_rt = int(max_rt)
+    exang = int(exang)
+    oldpeak = float(oldpeak)
+    slope = int(slope)
+    ca = int(ca)
+    thal = int(thal)
+
+    # Create the input array
+    input_array = np.array([age, sex, cp, restbp, chol, fbs, ecg, max_rt, exang, oldpeak, slope, ca, thal]).reshape(1,-1)
+
+    # Get the selected model
+    selected_model = models[model]
+
+    scaler = StandardScaler()
+    normalized_input_array = scaler.fit_transform(input_array)
+
+    predi = selected_model.predict(normalized_input_array)
+    # Convert the prediction to a string
+    if predi[0] == 1:
+        return "Absent"
+    elif predi[0] == 2:
+        return "Present"
+    else:
+        return "Unknown"
+
+# Create the interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=[
+        age_dropdown, sex_dropdown, cp_dropdown, restbp_dropdown, chol_dropdown,
+        fbs_dropdown, ecg_dropdown,max_rt_dropdown, exang_dropdown, oldpeak_dropdown,
+slope_dropdown, ca_dropdown, thal_dropdown, model_dropdown
+],
+  outputs = xoutputs,
+examples=[[70, 1.0,4.0,130.0,322.0,0.0,2.0,109.0,0.0,2.4,2.0,3.0,2.0,"SVC"]],
+
+title="Heart Disease Prediction",
+description="ML HomeWork 04"
+)
+
+interface.launch()