Update app.py

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+from PIL import Image
+import requests
+import hopsworks
+import joblib
+import pandas as pd
+
+hopsworks_iris_api_key = bP6PFOAzbXllM89C.l9gzwKTyxct786c3V1gwIQEvbQfZnSELxp7UM4RdBhw0eTaqkdl1Ld2a4A32UmR9
+project = hopsworks.login(api_key_value = hopsworks_iris_api_key)
+fs = project.get_feature_store()
+
+# Get the model from the registry on Hopsworks and load it
+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[0])
+    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)
+    print(img)            
+    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.Number(label="sepal length (cm)", default='2'),
+        gr.Number(label="sepal width (cm)"),
+        gr.Number(label="petal length (cm)"),
+        gr.Number(label="petal width (cm)"),
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch(debug=True)