Added expanders to results

app.py

@@ -102,8 +102,19 @@ def sort_by_rating(k):
     k - int - how many top-matching places to show
     '''
     relevance = np.array(st.session_state.precalculated_df['Relevancy'])
-    rating = np.array(list(st.session_state.precalculated_df['Rating']))
+    rating = np.array(st.session_state.precalculated_df['Rating'])
     top_similar_by_rating = dict(enumerate(np.multiply(relevance, rating)))
+    
+    ## Combine the three lists into a list of tuples (name, score, price)
+    # restaurant_data = list(zip(rest_names, relevance, rating))
+
+    # # Sort the combined list based on rating (index 2) in descending order and relevance (index 1) in descending order
+    # sorted_data = sorted(restaurant_data, key=lambda x: (-x[1], -x[2]))
+
+    # # Extract the sorted lists
+    # sorted_restaurant_names, sorted_relevance, sorted_rating = zip(*sorted_data)
+    # result = {sorted_restaurant_names[i]: sorted_relevance[i] for i in range(k)}
+
     st.session_state.precalculated_df['Sort_rating'] = top_similar_by_rating.values()
     
     #sort in the descending order
@@ -113,6 +124,7 @@ def sort_by_rating(k):
     #get restaurant names
     names = [rest_names[i] for i in top_k_similar.keys()]
     result = dict(zip(names, top_k_similar.values()))
+    
     return result
 
 #combines 2 users preferences into 1 string and fetches best options
@@ -240,9 +252,28 @@ css = """
         display: inline-block;
         padding: 5px 10px;
     }
+    
+    .violet-box {
+        border: 2px solid #004d00;  /* Dark violet contour */
+        border-radius: 10px;
+        background-color: #4CAF50;  /* violet background */
+        display: inline-block;
+        padding: 5px 10px;
+        color: #FFFFFF;  /* White text color */
+    }
+    
+    .violet-box {
+        border: 2px solid #8a2be2;  /* Violet contour */
+        border-radius: 10px;
+        background-color: #4169E1;  /* Blue background */
+        display: inline-block;
+        padding: 5px 10px;
+        color: #FFFFFF;  /* White text color */
+    }
 </style>
 """
 
+
 # options_disability_1 = st.multiselect(
 #      'Do you need a wheelchair?',
 #      ['Yes', 'No'], ['No'], key=101)
@@ -258,8 +289,9 @@ css = """
 # with st.form('my_form_1'):
 #     st.subheader('**User 1**')
 
-st.write("User 1")
-# Eingabe-Widgets
+st.markdown(css, unsafe_allow_html=True)
+st.markdown(f'<div class="violet-box">User 1</div>', unsafe_allow_html=True)
+
 food_1 = st.selectbox('Select the food type you prefer', st.session_state.food, key=1)
 if food_1 == 'Other':
     food_1 = st.text_input(label="Your description", placeholder="What kind of food would you like to eat?", key=10)
@@ -276,10 +308,9 @@ additional_1 = st.text_input(label="Your description", placeholder="Anything els
     
 with_kids = st.checkbox('I will come with kids', key=200)
     
-    # st.subheader('**User 2**')
-st.write("User 2")
+st.markdown(css, unsafe_allow_html=True)
+st.markdown(f'<div class="violet-box">User 2</div>', unsafe_allow_html=True)
 
-# Eingabe-Widgets
 food_2 = st.selectbox('Select the food type you prefer', st.session_state.food, key=3)
 if food_2 == 'Other':
     food_2 = st.text_input(label="Your description", placeholder="What kind of food would you like to eat?", key=4)
@@ -351,29 +382,36 @@ if submit or (not st.session_state.precalculated_df.empty):
                 
                 condition = st.session_state.precalculated_df['Names'] == name
                 rating = st.session_state.precalculated_df.loc[condition, 'Rating'].values[0]
-                st.write(f":{nums_emojis[i]}: **{name}** **({str(rating)}**:star:) :", 'match score:', score)
-    
-                # Use the condition to extract the value(s)
-                # description = st.session_state.precalculated_df.loc[condition, 'Strings']
-                # st.write(description)
-                
-                type = [item for item in eval(st.session_state.precalculated_df.loc[condition, 'Category'].values[0])]
-                # Display HTML with the custom styles
-                for word in type:
-                    st.markdown(css, unsafe_allow_html=True)
-                    st.markdown(f'<div class="blue-box">{word}</div>', unsafe_allow_html=True)
-                # st.write("Restaurant type:", str(type))
-                
-                keywords = [item[0] for item in eval(st.session_state.precalculated_df.loc[condition, 'Keywords'].values[0])]
-                for pair in keywords[:3]:
-                    st.markdown(css, unsafe_allow_html=True)
-                    st.markdown(f'<div class="orange-box">{pair[0]} {pair[1]}</div>', unsafe_allow_html=True)
-                # st.write("Restaurant type:", str(type))
-                
-                
-                url = st.session_state.precalculated_df.loc[condition, 'URL'].values[0]
-                st.write("_Check on the map:_", url)
-                
+                with st.expander(f":{nums_emojis[i]}: **{name}** **({str(rating)}**:star:): match score: {score}"):
+                    
+                    #f":{nums_emojis[i]}: **{name}** **({str(rating)}**:star:) :", 'match score:', score
+                    try:
+                        if type(st.session_state.precalculated_df.loc[condition, 'Price'].values[0]) == str:
+                            st.write("Price category:", st.session_state.precalculated_df.loc[condition, 'Price'].values[0])
+                    except:
+                        pass
+                        
+                    # Use the condition to extract the value(s)
+                    # description = st.session_state.precalculated_df.loc[condition, 'Strings']
+                    # st.write(description)
+                    
+                    type = [item for item in eval(st.session_state.precalculated_df.loc[condition, 'Category'].values[0])]
+                    # Display HTML with the custom styles
+                    for word in type:
+                        st.markdown(css, unsafe_allow_html=True)
+                        st.markdown(f'<div class="blue-box">{word}</div>', unsafe_allow_html=True)
+                    # st.write("Restaurant type:", str(type))
+                    
+                    keywords = [item[0] for item in eval(st.session_state.precalculated_df.loc[condition, 'Keywords'].values[0])]
+                    for pair in keywords[:3]:
+                        st.markdown(css, unsafe_allow_html=True)
+                        st.markdown(f'<div class="orange-box">{pair[0]} {pair[1]}</div>', unsafe_allow_html=True)
+                    # st.write("Restaurant type:", str(type))
+                    
+                    
+                    url = st.session_state.precalculated_df.loc[condition, 'URL'].values[0]
+                    st.write(f"_Check on the_ [_map_]({url})")
+                                    
                 i+=1
                             
                 # st.markdown("This is a text with <span style='font-size: 20px;'>bigger</span> and <i>italic</i> text.", unsafe_allow_html=True)