Upload app.py

app.py

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+import os
+import pickle
+import tensorflow as tf
+import gradio as gr
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+
+data_heading = ['longitude', 'latitude', 'housing_median_age', 'total_rooms',
+       'total_bedrooms', 'population', 'households', 'median_income',
+       'median_house_value']
+
+# Model and scaler loading
+with open("./model/scaler_sklearn.pkl", "rb") as f:
+    scaler = pickle.load(f)
+loaded_model = tf.keras.saving.load_model('./model/house_value_model.keras')
+
+def test_ml_model(longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, median_house_value):
+
+    df_test = pd.DataFrame(data=[longitude, latitude, housing_median_age,
+                            total_rooms, total_bedrooms, population,
+                            households, median_income, median_house_value], columns=data_heading)
+    df_test_norm = pd.DataFrame(scaler(df_test), columns=data_heading)
+    
+    result = loaded_model.predict(df_test_norm)
+    return (f'predicted: {result}')
+
+demo = gr.Interface(fn=test_ml_model,
+                    inputs=[gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0),
+                            gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0),
+                            gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0),
+                            gr.Number(value=0.0),], 
+                    outputs="text",
+                    description="A sample linear regressor solution.",
+                    title='Synthetic Data Linear Regressor Solution')
+    
+demo.launch()