Upload app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import requests
+
+import hopsworks
+import joblib
+import pandas as pd
+
+project = hopsworks.login()
+fs = project.get_feature_store()
+
+
+mr = project.get_model_registry()
+model = mr.get_model("titanic_modal", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/titanic_model.pkl")
+
+
+def titanic(pclass,age,sibsp,parch,fare,sex,embarked):
+    input_list = []
+    input_list.append(pclass)
+    input_list.append(age)
+    input_list.append(sibsp)
+    input_list.append(parch)
+    input_list.append(fare)
+    if sex == "male":
+        input_list.append(0)
+        input_list.append(1)
+    elif sex == "female":
+        input_list.append(1)
+        input_list.append(0)
+    
+    if embarked == "C":
+        input_list.append(1)
+        input_list.append(0)
+        input_list.append(0)
+        input_list.append(0)
+    elif embarked == "Q":
+        input_list.append(0)
+        input_list.append(1)
+        input_list.append(0)
+        input_list.append(0)
+    elif embarked == "S":
+        input_list.append(0)
+        input_list.append(0)
+        input_list.append(1)
+        input_list.append(0)
+    elif embarked == "Unknown":
+        input_list.append(0)
+        input_list.append(0)
+        input_list.append(0)
+        input_list.append(1)
+    
+    # input_df = pd.DataFrame(data=input_list, columns = ['Pclass', 'Age', 'SibSp', 'Parch', 
+    #                                                     'Fare', 'Sex_female','Sex_male', 
+    #                                                     'Embarked_C', 'Embarked_Q', 'Embarked_S',
+    #                                                     'Embarked_Unknown'])
+    # 'res' is a list of predictions returned as the label.
+    res = model.predict(np.asarray(input_list).reshape(1, -1))
+    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want 
+    # the first element.
+    if res[0] == 1:
+        res_str = "survivor"
+    else:
+        res_str = "victim"
+    passenger_url = "https://raw.githubusercontent.com/daniel-rdt/serverless_ml_titanic_dr/main/assets/" + res_str + ".png"
+    img = Image.open(requests.get(passenger_url, stream=True).raw)            
+    return img
+    # if res[0] == 1:
+    #     return "The passenger is predicted to be a survivor."
+    # else:
+    #     return "The passenger is predicted to be a victim."
+        
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Passenger Predictive Analytics",
+    description="Experiment with passenger data to predict whether the passenger is a survivor or not.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=2, label="Passenger class (choose from either 1, 2 or 3)"),
+        gr.inputs.Number(default=30, label="Age in full years (if child younger than 1 round up to 1)"),
+        gr.inputs.Number(default=1, label="Number of siblings or spouses"),
+        gr.inputs.Number(default=0, label="Number of parents or children"),
+        gr.inputs.Number(default=100, label="Fare (cost between 0 and 513)"),
+        gr.inputs.Textbox(default="male", label="Sex (choose from either male or female)"),
+        gr.inputs.Textbox(default="Unknown", label="Embarked (choose from either C, Q, S or Unknown)"),
+        ],
+    # outputs=gr.outputs.Textbox())
+    outputs=gr.Image(type="pil"))
+
+demo.launch()