Rename Deeploy Model Token to Deeploy API token

app.py

@@ -57,7 +57,7 @@ def predict_callback():
             print("Request body: ", request_body)
             # Call the explain endpoint as it also includes the prediction
             pred = client.predict(
-                request_body=request_body, deployment_id=deployment_id
+                request_body=st.session_state.request_body, deployment_id=deployment_id
             )
         except Exception as e:
             logging.error(e)
@@ -110,7 +110,7 @@ with st.sidebar:
     # Ask for model URL and token
     host = st.text_input("Host (Changing is optional)", "app.deeploy.ml")
     model_url, workspace_id, deployment_id = get_model_url()
-    deployment_token = st.text_input("Deeploy Model Token", "my-secret-token")
+    deployment_token = st.text_input("Deeploy API token", "my-secret-token")
     if deployment_token == "my-secret-token":
         st.warning("Please enter Deeploy API token.")
 
@@ -131,26 +131,102 @@ with st.sidebar:
 #     st.write(st.session_state)
 
 # Input (for IRIS dataset)
-with st.expander("Input values for prediction", expanded=True):
-    st.write("Please input the values for the model.")
-    col1, col2 = st.columns(2)
-    with col1:
-        sep_len = st.number_input("Sepal length", value=1.0, step=0.1, key="Sepal length")
-        sep_wid = st.number_input("Sepal width", value=1.0, step=0.1, key="Sepal width")
-    with col2:
-        pet_len = st.number_input("Petal length", value=1.0, step=0.1, key="Petal length")
-        pet_wid = st.number_input("Petal width", value=1.0, step=0.1, key="Petal width")
+# with st.expander("Input values for prediction", expanded=True):
+#     st.write("Please input the values for the model.")
+#     col1, col2 = st.columns(2)
+#     with col1:
+#         sep_len = st.number_input("Sepal length", value=1.0, step=0.1, key="Sepal length")
+#         sep_wid = st.number_input("Sepal width", value=1.0, step=0.1, key="Sepal width")
+#     with col2:
+#         pet_len = st.number_input("Petal length", value=1.0, step=0.1, key="Petal length")
+#         pet_wid = st.number_input("Petal width", value=1.0, step=0.1, key="Petal width")
 
 request_body = {
-    "instances": [
-        [
-            sep_len,
-            sep_wid,
-            pet_len,
-            pet_wid,
-        ],
+  "instances": [
+    [
+      20,
+      "RH",
+      80,
+      11622,
+      "Pave",
+      "missing",
+      "Reg",
+      "Lvl",
+      "AllPub",
+      "Inside",
+      "Gtl",
+      "NAmes",
+      "Feedr",
+      "Norm",
+      "1Fam",
+      "1Story",
+      5,
+      6,
+      1961,
+      1961,
+      "Gable",
+      "CompShg",
+      "VinylSd",
+      "VinylSd",
+      "NA",
+      0,
+      "TA",
+      "TA",
+      "CBlock",
+      "TA",
+      "TA",
+      "No",
+      "Rec",
+      468,
+      "LwQ",
+      144,
+      270,
+      882,
+      "GasA",
+      "TA",
+      "Y",
+      "SBrkr",
+      896,
+      0,
+      0,
+      896,
+      0,
+      0,
+      1,
+      0,
+      2,
+      1,
+      "TA",
+      5,
+      "Typ",
+      0,
+      "missing",
+      "Attchd",
+      1961,
+      "Unf",
+      1,
+      730,
+      "TA",
+      "TA",
+      "Y",
+      140,
+      0,
+      0,
+      0,
+      120,
+      0,
+      "missing",
+      "MnPrv",
+      "missing",
+      0,
+      6,
+      2010,
+      "WD",
+      "Normal"
     ]
+  ]
 }
+st.session_state.request_body = request_body
 
 # Predict and explain
 predict_button = st.button("Predict", on_click=predict_callback)

requirements.txt

@@ -1,3 +1,3 @@
-deeploy==1.2.1
+deeploy==1.37.4
 streamlit==1.29.0
 plotly==5.18.0