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
+
+project = hopsworks.login(api_key_value="4CY1rwa8iz8Yu6gG.TwayrYmsX4GQfhSp3LNKYTLvyFMfqAvnzNUQp5ae9K5HhfYxb5mcnLAutm1K18zV")
+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 passenger(pclass, sex, family, groupedage):
+    input_list = []
+    input_list.append(pclass)
+    input_list.append(sex)
+    input_list.append(family)
+    input_list.append(groupedage)
+    # '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:
+        passenger_url =  "https://cdn.pixabay.com/photo/2018/08/02/18/58/survival-3580200_960_720.png"
+    else:
+        passenger_url = "https://pngimg.com/uploads/death/death_PNG55.png"
+    
+    img = Image.open(requests.get(passenger_url, stream=True).raw)
+    return img
+#return res[0]         
+
+
+demo_titanic = gr.Interface(
+    fn=passenger,
+    title="Titanic Predictive Analytics",
+    description="Experiment to predict if a passenger survived or died in the titanic",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="Pclass (Min:1, Max=3"),
+        gr.inputs.Number(default=1.0, label="Sex (Female:0 and Male:1)"),
+        gr.inputs.Number(default=1.0, label="Family (Number of family members in the boat[0,7])"),
+        gr.inputs.Number(default=1.0, label="Age (Child:0, Adult:1 and Old:2)")
+        ], 
+    outputs=gr.Image(type="pil"))
+
+#outputs=gr.Label(num_top_classes=2)
+demo_titanic.launch()
+