Upload app.py with huggingface_hub

app.py

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+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from huggingface_hub import hf_hub_download
+import pandas as pd
+import gradio as gr
+import joblib
+
+
+MODEL_NAME = "mvs"
+HF_USER = "universalml"
+REPO_ID = HF_USER + "/" + MODEL_NAME
+model = joblib.load(hf_hub_download(repo_id=REPO_ID, filename="model.joblib"))
+
+
+def encode_categorical_columns(data_frame):
+    label_encoder = LabelEncoder()
+    ordinal_columns = data_frame.select_dtypes(include=['object']).columns
+
+    for col in ordinal_columns:
+        data_frame[col] = label_encoder.fit_transform(data_frame[col])
+
+    nominal_columns = data_frame.select_dtypes(include=['object']).columns.difference(ordinal_columns)
+    data_frame = pd.get_dummies(data_frame, columns=nominal_columns, drop_first=True)
+
+    return data_frame
+
+
+def prediction_function(*args):
+    values_list = []
+
+    for arg in args:
+        values_list.append(int(arg))
+
+    input_data_frame = pd.DataFrame(values_list, columns=model.input_feature_names)
+    data_frame = encode_categorical_columns(input_data_frame)
+    scaler = StandardScaler()
+    # Scale the input data using the loaded scaler
+    scaled_input = scaler.transform(data_frame)
+    prediction_result = model.predict(scaled_input)[0]
+
+    return prediction_result
+
+
+def regression_inputs():
+    input_labels = model.input_feature_names
+    inputs = []
+
+    for input_label in input_labels:
+        value = gr.Textbox(label=input_label, type="text")
+        inputs.append(value)
+
+    return inputs
+
+
+def regression_output():
+    output_label = model.output_feature_name
+    output = gr.Textbox(label=output_label, type="text")
+
+    return output
+
+
+def create_interface():
+    interface = gr.Interface(
+        fn=prediction_function,
+        inputs=regression_inputs(),
+        outputs=regression_output(),
+        title=MODEL_NAME,
+    )
+
+    interface.launch(debug=True)
+
+
+create_interface()