Update app.py

app.py

@@ -1,48 +1,25 @@
 import gradio as gr
 from PIL import Image
 import hopsworks
-import joblib
 
 project = hopsworks.login()
 fs = project.get_feature_store()
-mr = project.get_model_registry()
-model = mr.get_model("titanic_survival_modal", version=1)
-model_dir = model.download()
-model = joblib.load(model_dir + "/titanic_model.pkl")
-
-def tb_titanic(pclass,sex,age,sibsp,parch,embarked,fare_per_customer,cabin):
-    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(embarked)
-    input_list.append(fare_per_customer)
-    input_list.append(cabin)
-    # 'res' is a list of predictions returned as the label.
-    #global res
-    res = model.predict(np.asarray(input_list).reshape(1, 8))
-    if res[0]=="S":
-       return ("survived")
-    else:
-       return("deceased")
-  
-
 
 dataset_api = project.get_dataset_api()
+
+dataset_api.download("Resources/images/latest_survived.png")
+dataset_api.download("Resources/images/actual_survived.png")
 dataset_api.download("Resources/images/df_recent.png")
 dataset_api.download("Resources/images/confusion_matrix.png")
 
 with gr.Blocks() as demo:
-    fn=tb_titanic,
     with gr.Row():
       with gr.Column():
-          gr.Label("Today's Predicted survive")
-          input=gr.Textbox()
+          gr.Label("Today's Predicted Image")
+          input_img = gr.Image("latest_survived.png", elem_id="predicted-img")
       with gr.Column():          
-          gr.Label("Today's Actual survive")
-          input=gr.Textbox()
+          gr.Label("Today's Actual Image")
+          input_img = gr.Image("actual_survived.png", elem_id="actual-img")        
     with gr.Row():
       with gr.Column():
           gr.Label("Recent Prediction History")