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README.md

@@ -1,17 +1,13 @@
 ---
 title: Iris
 emoji: 🐢
-colorFrom: blue
-colorTo: gray
+colorFrom: purple
+colorTo: green
 sdk: gradio
-sdk_version: 4.1.2
+sdk_version: 3.5
 app_file: app.py
 pinned: false
 license: apache-2.0
-requirements:
-  - file: requirements.txt
-environment:
-  HOPSWORKS_API_KEY: ${{ secrets.HOPSWORKS_API_KEY }}
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,30 +1,49 @@
 import gradio as gr
+from PIL import Image
+import requests
 import hopsworks
 import joblib
 import pandas as pd
-import numpy as np
+
 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")
-feature_view = fs.get_feature_view(name="iris", version=1)
-batch_data = feature_view.get_batch_data()
-
-def greet(sep_length, sep_width, pet_length, pet_width):
-    df = pd.DataFrame({ "sepal_length": sep_length, "sepal_width": sep_width, "petal_length": pet_length, "petal_width": pet_width}, index=[0])
-    prediction = model.predict(df)[0]
-    return prediction, f"Images\{prediction}.jpg"
+print("Model downloaded")
 
-predict = gr.Interface(
-    fn=greet,
+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.Textbox(placeholder="Sepal length here", label = "Sepal Length"),
-        gr.Textbox(placeholder="Sepal width here", label = "Sepal Width"),
-        gr.Textbox(placeholder="Petal length here", label = "Petal Length"),
-        gr.Textbox(placeholder="Petal width here", label = "Petal Width")],
-    outputs=[gr.Textbox(label = "Prediction"), gr.Image(type="pil", label="Iris")],
-)
-predict.launch()
+        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

@@ -1,3 +1,3 @@
 hopsworks
 joblib
-sklearn
+scikit-learn==1.1.1