upload iris files

app.py

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+import gradio as gr
+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("iris_model", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/iris_model.pkl")
+print("Model downloaded")
+
+def iris(sepal_length, sepal_width, petal_length, petal_width):
+    print("Calling function")
+#     df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]], 
+    df = pd.DataFrame([[sepal_length,sepal_width,petal_length,petal_width]], 
+                      columns=['sepal_length','sepal_width','petal_length','petal_width'])
+    print("Predicting")
+    print(df)
+    # 'res' is a list of predictions returned as the label.
+    res = model.predict(df) 
+    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want 
+    # the first element.
+#     print("Res: {0}").format(res)
+    print(res)
+    flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png"
+    img = Image.open(requests.get(flower_url, stream=True).raw)            
+    return img
+        
+demo = gr.Interface(
+    fn=iris,
+    title="Iris Flower Predictive Analytics",
+    description="Experiment with sepal/petal lengths/widths to predict which flower it is.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=2.0, label="sepal length (cm)"),
+        gr.inputs.Number(default=1.0, label="sepal width (cm)"),
+        gr.inputs.Number(default=2.0, label="petal length (cm)"),
+        gr.inputs.Number(default=1.0, label="petal width (cm)"),
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch(debug=True)
+

requirements.txt

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+hopsworks
+joblib
+scikit-learn==1.1.1