Update app.py

app.py

@@ -10,42 +10,22 @@ project = hopsworks.login(api_key_value="0rdWXlLgEd3mkGOg.iRZ7TtAkWGPlJHNQcAEph6
 fs = project.get_feature_store()
 
 mr = project.get_model_registry()
-model = mr.get_model("titanic_modal", version=1)
+model = mr.get_model("xgboost_model", version=1)
 model_dir = model.download()
-model = joblib.load(model_dir + "/titanic_model.pkl")
+model = joblib.load(model_dir + "/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
-
+def forecast():
+    x = 0
+    #res = model.predict(x)
+    
+    return 10
 
 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.",
+    title="Air Quality Prediction",
+    description="Get aqi value",
     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()