improve app

app.py

@@ -1,6 +1,6 @@
 import copy
 import enum
-import os
+import pandas as pd
 from typing import List, Optional
 
 import requests
@@ -44,29 +44,35 @@ def display_predictions(
     model_names: List[str],
     threshold: Optional[float] = None,
 ):
-    debug_showed = False
+    debug = None
     for model_name in model_names:
         response = get_predictions(barcode, model_name, threshold)
         response = copy.deepcopy(response) 
-        if "debug" in response:
-            if not debug_showed:
-                debug_showed = True
-                st.write(response["debug"])
+        if model_name != NeuralCategoryClassifierModel.keras_2_0.name and "debug" in response:
+            if debug is None:
+                debug = response["debug"]
             response.pop("debug")
-        st.write(f"** {model_name} **")
-        st.write(response)
+        st.markdown(f"**{model_name}**")
+        st.write(pd.DataFrame(response["predictions"]))
+    
+    if debug is not None:
+        st.markdown("**v3 debug information**")
+        st.write(debug)
 
 
 
 st.sidebar.title("Category Prediction Demo")
+query_params = st.experimental_get_query_params()
+
+default_barcode = query_params["barcode"][0] if "barcode" in query_params else ""
 barcode = st.sidebar.text_input(
-    "Product barcode"
+    "Product barcode", default_barcode
 )
 threshold = st.sidebar.number_input("Threshold", format="%f") or None
 model_names = st.multiselect(
     "Name of the model",
     [x.name for x in NeuralCategoryClassifierModel],
-    default=NeuralCategoryClassifierModel.keras_sota_3_0.name,
+    default=[x.name for x in NeuralCategoryClassifierModel],
 )
 
 if barcode: