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README.md

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----
-title: Titanic
-emoji: 🐠
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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()
+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 iris(pclass, sex, age, sibsp,parch,fare,embarked):
+    input_list = []
+    input_list.append(pclass)
+    input_list.append(sex)
+    input_list.append(age)
+    input_list.append(sibsp)
+    input_list.append(parch)
+    input_list.append(fare)
+    input_list.append(embarked)
+    # '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.
+    survival_url = "https://raw.githubusercontent.com/freeja/id2223/main/serverless-titanic/" + str(res[0]) + ".png"
+    img = Image.open(requests.get(survival_url, stream=True).raw)            
+    return img
+        
+demo = gr.Interface(
+    fn=iris,
+    title="Titanic Survival Analytics",
+    description="Experiment with passenger stats to predict the outcome of their journey.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="pclass (1-3)"),
+        gr.inputs.Number(default=1.0, label="sex (male:0, female:1)"),
+        gr.inputs.Number(default=1.0, label="age (1-80)"),
+        gr.inputs.Number(default=1.0, label="sibsp (cm)"),
+        gr.inputs.Number(default=1.0, label="parch (0-4)"),
+        gr.inputs.Number(default=1.0, label="fare (0-512)"),
+        gr.inputs.Number(default=1.0, label="embarked (1-3)"),
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch()
+

requirements.txt

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+hopsworks
+joblib
+scikit-learn