titanic

app.py

@@ -1,19 +1,34 @@
-# import gradio as gr
-# import numpy as np
-# from PIL import Image
-# import requests
+import gradio as gr
+import numpy as np
+from PIL import Image
+import requests
 
-# import hopsworks
-# import joblib
+import hopsworks
+import joblib
+
+project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU")
+fs = project.get_feature_store()
 
-# project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU")
-# 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")
 
-# 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 titanic(pclass, sex, age, sibsp, parch, fare, embarked):
+    input_list = []
+    input_list.append(pclass)
+    input_list.append(sex)
+    input_list.append(age)
+    input_list.append(sibsp)
+    input_list.append(parch)
+    input_list.append(fare)
+    input_list.append(embarked)
+    # '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.
+    return res[0]
 
 
 # def iris(sepal_length, sepal_width, petal_length, petal_width):
@@ -30,49 +45,53 @@
 #     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 = gr.Interface(
+    fn=titanic,
+    title="Titanic Predictive Analytics",
+    description="Experiment to predict if a passenger survived the Titanic disaster",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="PClass"),
+        gr.inputs.Number(default=1.0, label="Sex"),
+        gr.inputs.Number(default=1.0, label="Age"),
+        gr.inputs.Number(default=1.0, label="SibSp"),
+        gr.inputs.Number(default=1.0, label="Parch"),
+        gr.inputs.Number(default=1.0, label="Fare"),
+        gr.inputs.Number(default=1.0, label="Embarked")
+        ],
+    outputs=gr.Textbox())
 
-# demo.launch()
+demo.launch()
 
-import gradio as gr
-from PIL import Image
-import hopsworks
+# monitoring part of the code
+# import gradio as gr
+# from PIL import Image
+# import hopsworks
 
-project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU")
-fs = project.get_feature_store()
-#h
-dataset_api = project.get_dataset_api()
+# project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU")
+# fs = project.get_feature_store()
+# #h
+# dataset_api = project.get_dataset_api()
 
-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")
+# 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")        
+# 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()
+# demo.launch()