'initial'

.hw_api_key

@@ -0,0 +1 @@
+gJHnXNU7BFSJKue3.9GJb8V4qJRXE7Vn25uM6mDb39eOZIstYWJZ8XbgU7Fo6rlCZlzE3JNkcwDAYaAUJ

README.md

@@ -1,10 +1,10 @@
 ---
-title: Titanic
-emoji: 🐠
-colorFrom: pink
-colorTo: pink
+title: Iris
+emoji: 🐢
+colorFrom: purple
+colorTo: green
 sdk: gradio
-sdk_version: 3.9.1
+sdk_version: 3.5
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+import numpy as np
+from PIL import Image
+import requests
+import pandas as pd
+
+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")
+
+df = pd
+features = pd.read_csv(
+    "https://raw.githubusercontent.com/Nathanotal/remoteFiles/main/titanicCleaned.csv")
+features = features.drop(columns=["survived"])
+featureLabels = features.columns
+
+
+def titanic(input_list):
+    # '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.
+    # Todo: survivor, "https://fakeface.rest/face/json?maximum_age=50&gender=female&minimum_age=49"
+    survivor_url = 'https://picsum.photos/200/300'
+    img = Image.open(requests.get(survivor_url, stream=True).raw)
+    return img
+
+
+inputs = []
+for feature in featureLabels:
+    inputs.append(gr.inputs.Number(minimum=0, default=1.0, label=feature))
+
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Survivor Predictive Analytics",
+    description="Experiment with person features to predict which survivor it is.",
+    allow_flagging="never",
+    inputs=inputs,
+    outputs=gr.Image(type="pil"))
+
+demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+hopsworks
+joblib
+scikit-learn
+gradio