add app

app.py

@@ -0,0 +1,111 @@
+import gradio as gr
+import numpy as np
+from PIL import Image
+import requests
+
+import hopsworks
+import joblib
+
+project = hopsworks.login()
+fs = project.get_feature_store()
+
+
+mr = project.get_model_registry()
+model = mr.get_model("titanic_modal", version=4)
+model_dir = model.download()
+model = joblib.load(model_dir + "/titanic_model.pkl")
+
+
+def iris(Pclass, Sex, SibSp, Parch, Embarked, Age, Fare_type):
+                
+    input_list = []
+    input_list.append(Pclass)
+    
+    ##############################
+    if Sex=='male':
+        input_list.append(1)
+    else:
+        input_list.append(0)
+    ##############################
+    
+    input_list.append(SibSp)
+    input_list.append(Parch)
+
+    ##############################  
+    if Embarked=='S':
+        input_list.append(0)
+    elif Embarked=='C':
+        input_list.append(1)
+    else:
+        input_list.append(2)
+    ##############################    
+    
+    ##############################
+    if Age <= 12:
+        one_hot_age = [1, 0, 0, 0]
+        for i in one_hot_age:
+            input_list.append(i)
+    elif Age <= 19:
+        one_hot_age = [0, 1, 0, 0]
+        for i in one_hot_age:
+            input_list.append(i)
+    elif Age <= 39:
+        one_hot_age = [0, 0, 1, 0]
+        for i in one_hot_age:
+            input_list.append(i)
+    else:
+        one_hot_age = [0, 0, 0, 1]
+        for i in one_hot_age:
+            input_list.append(i)
+    ##############################
+    
+    ##############################
+    if Fare_type == "low":
+        one_hot_fare = [1, 0, 0, 0]
+        for i in one_hot_fare:
+            input_list.append(i)
+    elif Fare_type == "medium-low":
+        one_hot_fare = [0, 1, 0, 0]
+        for i in one_hot_fare:
+            input_list.append(i)
+    elif Fare_type == "medium": 
+        one_hot_fare = [0, 0, 1, 0]
+        for i in one_hot_fare:
+            input_list.append(i)
+    else:
+        one_hot_fare = [0, 0, 0, 1]
+        for i in one_hot_fare:
+            input_list.append(i)
+    ##############################
+    
+    # '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] == 0:
+        img_string =  "dead"
+    else:
+        img_string = "survived"
+        
+    passenger_url = "https://raw.githubusercontent.com/santroma1/id2223_lab1_titanic/main/assets/"  + img_string + ".jpg"
+    img = Image.open(requests.get(passenger_url, stream=True).raw)            
+    return img
+        
+demo = gr.Interface(
+    fn=iris,
+    title="Titanic Predictive Analytics",
+    description="Experiment to predict whether a passanger survived or not.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="Cabin class (1, 2, 3)"),
+        gr.Textbox(default='male', label="Sex (male, female)"),
+        gr.inputs.Number(default=1.0, label="SibSp (number of siblings/spouses aboard)"),
+        gr.inputs.Number(default=1.0, label="Parch (number of parents/children aboard)"),
+        gr.Textbox(default="S", label="Port of Embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)"),
+        gr.inputs.Number(default=1.0, label="Age"),
+        gr.Textbox(default="low", label="Fare_type (low, medium-low, medium, high)"),
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch(share=True)

requirements.txt

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+hopsworks
+joblib
+scikit-learn
+numpy