Upload app.py

app.py

@@ -1,31 +1,53 @@
 import gradio as gr
+import numpy as np
 from PIL import Image
+import requests
+
 import hopsworks
+import joblib
 
-project = hopsworks.login(api_key_value="U1TTeOPaUDFWhd6N.H604QVLj5yOFVPGeSrXsoFY2IKrGkdqR0iTzWMr22rZxXQrn5VoYKdb4fghqxTna")
+project = hopsworks.login()
 fs = project.get_feature_store()
 
-dataset_api = project.get_dataset_api()
-
-dataset_api.download("Resources/images/latest_passenger.png",overwrite=True)
-dataset_api.download("Resources/images/actual_passenger.png",overwrite=True)
-dataset_api.download("Resources/images/df_recent.png",overwrite=True)
-dataset_api.download("Resources/images/confusion_matrix.png",overwrite=True)
-
-with gr.Blocks() as demo:
-    with gr.Row():
-      with gr.Column():
-          gr.Label("Today's Predicted Image")
-          input_img = gr.Image("latest_passenger.png", elem_id="predicted-img")
-      with gr.Column():          
-          gr.Label("Today's Actual Image")
-          input_img = gr.Image("actual_passenger.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")        
+#HwJaWmtvaCzFra3g.89QYueFGuScRnJkiepzG2tiWtKSrqNHCCJrnVie9fwhIMeJxRUpAGAT7mF36MDMv
+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):
+    input_list = []
+    input_list.append(Pclass)
+    input_list.append(Sex)
+    input_list.append(Age)
+    input_list.append(SibSp)
+    # '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
+    if (res[0] == 0):
+        result = "I'm sorry, the person is dead"
+    else:
+        result = "Awesome, the person is survived!!!!!!"
+    return result
+
+
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Predictive Analytics",
+    description="Experiment with Passenger class/Sex/Age/SibSp to predict if the person is survived or not.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="Pclass (Flight class 1/2/3)"),
+        gr.inputs.Number(default=1.0, label="Sex (male=1/female=2)"),
+        gr.inputs.Number(default=1.0, label="Age (in years)"),
+        gr.inputs.Number(default=1.0, label="SibSp (number of siblings)"),
+    ],
+    outputs=gr.Textbox(label="Result: "))
 
 demo.launch()
+