Update app.py

app.py

@@ -67,7 +67,7 @@ def get_recommendations(user_description, count_vectorizer, count_matrix):
     cosine_similarities = cosine_similarity(user_vector, count_matrix).flatten()
     similar_indices = cosine_similarities.argsort()[::-1]
     return similar_indices
-@st.experimental_fragment
+@st.fragment
 def show_recipe(recipe):
     with spinner("HANG TIGHT, RECIPE INCOMING..."):
         name_and_dis = f'# {recipe["name"]}\n\n'
@@ -93,7 +93,7 @@ def show_recipe(recipe):
         with col02:
             cont = container(border=True, height=500)
             with cont:st.write(instructions)
-@st.experimental_fragment
+@st.fragment
 def cooking():
     client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
@@ -315,7 +315,7 @@ def cooking():
 
     if st.session_state.recipe is not None:
         st.divider()
-        print(st.session_state.recipe)
+        # print(st.session_state.recipe)
         recipe = json.loads(st.session_state.recipe)
         show_recipe(recipe)
         recipe['timestamp'] = str(datetime.datetime.now())
@@ -350,7 +350,7 @@ def rsaved():
     # get all saved files
     directory_path = 'data'
     recipes = load_saved_recipes_from_pickle(directory_path)
-    print(recipes)
+    # print(recipes)
 
     cols = st.columns([4, 1])
     with cols[1]:
@@ -376,7 +376,7 @@ def rsaved():
             show_recipe(filtered_recipes[0])
         else:
             st.write("No recipe found.")
-@st.experimental_fragment
+@st.fragment
 def mart():
     st.markdown("# :eyes: PEOPLE SEARCHING FOR...")
     print(st.session_state.ind)
@@ -432,7 +432,7 @@ def mart():
             with expander("## :desktop_computer: LOOKING FOR SOMETHING ELSE..."):
                 pygwalerapp(df).explorer()
 # Initialize the inference client for the Mixtral model
-@st.experimental_fragment
+@st.fragment
 def tabs():
     return st.tabs(['COOK','SAVED'])
 if not mode: