add checkbox

app.py

@@ -27,53 +27,6 @@ feature_view = fs.get_or_create_feature_view(
 )
 diabetes_df = pd.DataFrame(diabetes_fg.read())
 
-
-# def plot_reference(age, bmi, hba1c_level, blood_glucose_level):
-#     mean_for_age = diabetes_df[
-#         (diabetes_df["diabetes"] == 0) & (diabetes_df["age"] == age)
-#     ].mean()
-#     print("your bmi is:", bmi, "the mean for ur age is :", mean_for_age["bmi"])
-#     categories = ["Age", "BMI", "HbA1c", "Blood Level"]
-#     fig, ax = plt.subplots()
-#     bar_width = 0.35
-#     indices = np.arange(len(categories))
-#     ax.bar(
-#         indices,
-#         [mean_for_age.bmi, mean_for_age.hba1c_level, mean_for_age.blood_glucose_level],
-#         bar_width,
-#         label="Reference",
-#         color="b",
-#         alpha=0.7,
-#     )
-#     ax.bar(
-#         indices + bar_width,
-#         [bmi, hba1c_level, blood_glucose_level],
-#         bar_width,
-#         label="User",
-#         color="r",
-#         alpha=0.7,
-#     )
-
-#     ax.set_xlabel("Variables")
-#     ax.set_ylabel("Values")
-#     ax.set_title("Comparison of Reference and User Values")
-#     ax.set_xticks(indices + bar_width / 2)
-#     ax.set_xticklabels(categories)
-
-
-# def diabetes(age, bmi, hba1c_level, blood_glucose_level):
-#     print("Calling diabetes function")
-#     df = pd.DataFrame(
-#         [[age, bmi, hba1c_level, blood_glucose_level]],
-#         columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
-#     )
-#     print("Predicting")
-#     print(df)
-#     res = model.predict(df)
-#     print(res)
-#     return res
-
-
 with gr.Blocks() as demo:
     with gr.Row():
         gr.HTML(value="<h1 style='text-align: center;'>Diabetes prediction</h1>")
@@ -87,6 +40,13 @@ with gr.Blocks() as demo:
             blood_glucose_input = gr.Slider(
                 80, 300, label="blood_glucose_level", info="Blood Glucose Level"
             )
+            gr.Radio(
+                ["yes", "no"],
+                label="if you already know that you have diabetes, select yes, else select no",
+            )
+            gr.Checkbox(
+                info="I consent that my personal data can be used to retrain the model"
+            )
             btn = gr.Button("Submit")
         with gr.Column():
             output = gr.Text(label="Model prediction")
@@ -133,10 +93,11 @@ with gr.Blocks() as demo:
 
         ax.set_xlabel("Variables")
         ax.set_ylabel("Values")
-        ax.set_title("Comparison of Reference and User Values")
+        ax.set_title("Comparison with Mean values for your age")
         ax.set_xticks(indices + bar_width / 2)
         ax.set_xticklabels(categories)
 
+        ## save user's data in hopsworks
         return res, fig
 
     btn.click(
@@ -144,18 +105,5 @@ with gr.Blocks() as demo:
         inputs=[age_input, bmi_input, hba1c_input, blood_glucose_input],
         outputs=[output, plot],
     )
-# demo = gr.Interface(
-#     fn=diabetes,
-#     title="Diabetes Prediction",
-#     description="Fill these values with yours",
-#     allow_flagging="never",
-#     inputs=[
-#         gr.Number(label="age"),
-#         gr.Slider(10, 100, label="bmi", info="Body Mass Index"),
-#         gr.Slider(3.5, 9, label="hba1c_level", info="Glycated Haemoglobin"),
-#         gr.Slider(80, 300, label="blood_glucose_level", info="Blood Glucose Level"),
-#     ],
-#     outputs=gr.Number(label="diabetes"),
-# )
 
 demo.launch()