try to add stats

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 import hopsworks
 import joblib
 import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
 
 project = hopsworks.login(project="SonyaStern_Lab1")
 fs = project.get_feature_store()
@@ -26,35 +28,134 @@ feature_view = fs.get_or_create_feature_view(
 diabetes_df = pd.DataFrame(diabetes_fg.read())
 
 
-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"],
+# 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>")
+    with gr.Row():
+        with gr.Column():
+            age_input = gr.Number(label="age")
+            bmi_input = gr.Slider(10, 100, label="bmi", info="Body Mass Index")
+            hba1c_input = gr.Slider(
+                3.5, 9, label="hba1c_level", info="Glycated Haemoglobin"
+            )
+            blood_glucose_input = gr.Slider(
+                80, 300, label="blood_glucose_level", info="Blood Glucose Level"
+            )
+            btn = gr.Button("Submit")
+        with gr.Column():
+            output = gr.Text(label="Model prediction")
+            plot = gr.Plot()
+
+    def submit_inputs(age_input, bmi_input, hba1c_input, blood_glucose_input):
+        df = pd.DataFrame(
+            [[age_input, bmi_input, hba1c_input, blood_glucose_input]],
+            columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
+        )
+        res = model.predict(df)
+
+        mean_for_age = diabetes_df[
+            (diabetes_df["diabetes"] == 0) & (diabetes_df["age"] == age_input)
+        ].mean()
+
+        print(
+            "your bmi is:", bmi_input, "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_input, hba1c_input, blood_glucose_input],
+            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)
+
+        return res, fig
+
+    btn.submit(
+        submit_inputs,
+        [age_input, bmi_input, hba1c_input, blood_glucose_input],
+        [output, plot],
     )
-    print("Predicting")
-    print(df)
-    res = model.predict(df)
-    print(res)
-    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"])
-    return res
-
-
-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 = 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(debug=True)

requirements.txt

@@ -2,4 +2,6 @@ gradio
 hopsworks
 joblib
 pandas
-scikit-learn==1.1.1
+scikit-learn==1.1.1
+matplotlib
+numpy