Create app.py

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+import numpy as np
+
+MODEL_NAME = "model.pkl"
+gradio_labels = ["Gender",
+           "Age",
+           "Family history with overweight",
+           "Frequent consumption of high caloric food (FAVC)",
+           "Frequency of consumption of vegetables (FCVC)",
+           "Number of main meals (NCP)",
+           "Consumption of food between meals (CAEC)",
+           "Smoking habit (SMOKE)",
+           "Consumption of water daily (CH2O)",
+           "Calories consumption monitoring (SCC)",
+           "Physical activity frequency (FAF)",
+           "Time using technology devices (TUE)",
+           "Consumption of alcohol (CALC)",
+           "Transportation used (MTRANS)"]
+columns = ["Gender",
+           "Age",
+           "family_history_with_overweight",
+           "FAVC",
+           "FCVC",
+           "NCP",
+           "CAEC",
+           "SMOKE",
+           "CH2O",
+           "SCC",
+           "FAF",
+           "TUE",
+           "CALC",
+           "MTRANS"]
+
+# this is a fake function to learn gradio, not really use
+def dummy_function(*inputs):
+    df = pd.Series(inputs)
+    print(df)
+    pred = np.random.randint(1, 9)
+    return pred
+pred = dummy_function()
+
+def predict_obesity(*inputs):
+    model = joblib.load(MODEL_NAME)
+    df = pd.DataFrame([inputs], columns=columns)
+    pred = model.predict(df)[0]
+    pred_string = {
+        1: "Insufficient Weight",
+        2: "Normal Weight",
+        3: "Over Weight I",
+        4: "Over Weight II",
+        5: "Over Weight III" ,
+        6: "Obesity I",
+        7: "Obesity II",
+        8: "Obesity III",
+    }
+    final_pred = pred
+    if isinstance(pred, int):
+        final_pred = pred_string[pred]
+    return final_pred
+
+interface = gr.Interface(
+    fn=predict_obesity,
+    inputs=[gr.Dropdown(label=gradio_labels[0], choices=["Male", "Female"]),
+            gr.Textbox(label=gradio_labels[1]),
+            gr.Dropdown(label=gradio_labels[2], choices=["yes", "no"]),
+            gr.Dropdown(label=gradio_labels[3], choices=["yes", "no"]),
+            gr.Slider(label=gradio_labels[4], minimum=1, maximum=3),
+            gr.Slider(label=gradio_labels[5], minimum=1, maximum=4),
+            gr.Dropdown(label=gradio_labels[6], choices=['Sometimes', 'Frequently', 'Always', 'no']),
+            gr.Dropdown(label=gradio_labels[7], choices=['no', 'yes']),
+            gr.Slider(label=gradio_labels[8], minimum=1, maximum=3),
+            gr.Dropdown(label=gradio_labels[9], choices=['no', 'yes']),
+            gr.Slider(label=gradio_labels[10], minimum=0, maximum=3),
+            gr.Slider(label=gradio_labels[11], minimum=0, maximum=2),
+            gr.Dropdown(label=gradio_labels[12], choices=['no', 'Sometimes', 'Frequently', 'Always']),
+            gr.Dropdown(label=gradio_labels[13], choices=['Public_Transportation', 'Walking', 'Automobile', 'Motorbike', 'Bike'])
+            ],
+    outputs=gr.Textbox(label="Predicted Obesity Level"),
+    title="Obesity Level Prediction"
+    )
+
+interface.launch()