gg2.0

app.py

@@ -1,46 +1,78 @@
+# import gradio as gr
+# import numpy as np
+# from PIL import Image
+# import requests
+
+# import hopsworks
+# import joblib
+
+# project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU")
+# fs = project.get_feature_store()
+
+
+# mr = project.get_model_registry()
+# model = mr.get_model("iris_modal", version=1)
+# model_dir = model.download()
+# model = joblib.load(model_dir + "/iris_model.pkl")
+
+
+# def iris(sepal_length, sepal_width, petal_length, petal_width):
+#     input_list = []
+#     input_list.append(sepal_length)
+#     input_list.append(sepal_width)
+#     input_list.append(petal_length)
+#     input_list.append(petal_width)
+#     # '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.
+#     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=1.0, label="sepal length (cm)"),
+#         gr.inputs.Number(default=1.0, label="sepal width (cm)"),
+#         gr.inputs.Number(default=1.0, label="petal length (cm)"),
+#         gr.inputs.Number(default=1.0, label="petal width (cm)"),
+#         ],
+#     outputs=gr.Image(type="pil"))
+
+# demo.launch()
+
 import gradio as gr
-import numpy as np
 from PIL import Image
-import requests
-
 import hopsworks
-import joblib
 
 project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU")
 fs = project.get_feature_store()
 
+dataset_api = project.get_dataset_api()
 
-mr = project.get_model_registry()
-model = mr.get_model("iris_modal", version=1)
-model_dir = model.download()
-model = joblib.load(model_dir + "/iris_model.pkl")
-
-
-def iris(sepal_length, sepal_width, petal_length, petal_width):
-    input_list = []
-    input_list.append(sepal_length)
-    input_list.append(sepal_width)
-    input_list.append(petal_length)
-    input_list.append(petal_width)
-    # '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.
-    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=1.0, label="sepal length (cm)"),
-        gr.inputs.Number(default=1.0, label="sepal width (cm)"),
-        gr.inputs.Number(default=1.0, label="petal length (cm)"),
-        gr.inputs.Number(default=1.0, label="petal width (cm)"),
-        ],
-    outputs=gr.Image(type="pil"))
-
-demo.launch()
+dataset_api.download("Resources/images/latest_iris.png")
+dataset_api.download("Resources/images/actual_iris.png")
+dataset_api.download("Resources/images/df_recent.png")
+dataset_api.download("Resources/images/confusion_matrix.png")
+
+with gr.Blocks() as demo:
+    with gr.Row():
+      with gr.Column():
+          gr.Label("Today's Predicted Image")
+          input_img = gr.Image("latest_iris.png", elem_id="predicted-img")
+      with gr.Column():          
+          gr.Label("Today's Actual Image")
+          input_img = gr.Image("actual_iris.png", elem_id="actual-img")        
+    with gr.Row():
+      with gr.Column():
+          gr.Label("Recent Prediction History")
+          input_img = gr.Image("df_recent.png", elem_id="recent-predictions")
+      with gr.Column():          
+          gr.Label("Confusion Maxtrix with Historical Prediction Performance")
+          input_img = gr.Image("confusion_matrix.png", elem_id="confusion-matrix")        
+
+demo.launch()