gg yet again

app.py

@@ -6,92 +6,70 @@ 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("titanic_modal", version=1)
-model_dir = model.download()
-model = joblib.load(model_dir + "/titanic_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]
+# project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU")
+# fs = project.get_feature_store()
+# #q
 
+# 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")
 
-# def iris(sepal_length, sepal_width, petal_length, petal_width):
+# def titanic(pclass, sex, age, sibsp, parch, fare, embarked):
 #     input_list = []
-#     input_list.append(sepal_length)
-#     input_list.append(sepal_width)
-#     input_list.append(petal_length)
-#     input_list.append(petal_width)
+#     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 
+#     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
+#     return res[0]
+
         
-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 = 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: Female = 0, Male = 1"),
+#         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: S = 0, C = 1, Q = 2"),
+#         ],
+#     outputs=gr.Textbox())
 
-demo.launch()
+# demo.launch()
 
 # monitoring part of the code
-# import gradio as gr
-# from PIL import Image
-# import hopsworks
+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/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("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()