update app.py to include datavault

app.py

@@ -66,7 +66,7 @@ def display_image(url, scale=0.5):
     image = Image.open(urlopen(url))
     st.image(image.resize(( int(image.width * scale), int(image.height * scale))))
 
-def display_heroes_from_df(df):
+def display_heroes_from_df(df,display_cols=display_cols):
     st.dataframe(df[display_cols],
                  column_config={
                          "image": st.column_config.ImageColumn("Avatar", help="")},
@@ -76,17 +76,82 @@ def display_heroes_from_df(df):
     for i in range(len(df)):
         url = df['image'].values[i]
         display_image(url)
-        st.write(f"{df['name'].values[i]} - {df['speed'].values[i]} - {df['class'].values[i]}")
+        st.write(f"***{df['name'].values[i]}*** - {df['speed'].values[i]} - {df['class'].values[i]}")
         st.write(f'Attack:{df["attack"].values[i]} -- Defence:{df["defense"].values[i]} -- Health:{df["health"].values[i]}')
-        st.write(df['skill'].values[i])
+        st.write(f"***{df['skill'].values[i]}***" )
         st.write(df['effects'].values[i])
         # for sp in df['effects'].values[i]:
         #     st.write(sp)
 
+#########################################
+## Helper function for LB/CB stat analysis
+def return_costume_list(df0, hero_name):
+    assert hero_name in df0.name.values
+
+    if hero_name[-2:] == "C2":
+        return ['None', 'CB1', 'CB2']
+    elif hero_name[-2:] == " C":
+        hero_name2 = hero_name + "2"
+        if hero_name2 in df0.name.values: # if this hero has C2
+            return ['None', 'CB1', 'CB2']
+        else:
+            return ['None', 'CB1']
+    else:
+        hero_name1 = hero_name + " C"
+        hero_name2 = hero_name + " C2"
+        if hero_name2 in df0.name.values: # if this hero has C2
+            return ['None', 'CB1', 'CB2']
+        elif hero_name1 in df0.name.values: # if this hero has C2
+            return ['None', 'CB1']
+        else:
+            return ['None']
+
+def get_prefix(lb_choice="None", costume_choice="None"):
+    prefix_1 = "Max level"
+
+    if lb_choice != 'None':
+        prefix_1 = "Limit Break"
+    
+    prefix_2 = ""
+    if costume_choice != "None":
+        prefix_2 = f" {costume_choice}" # CB1 or CB2
+
+    prefix_3 = ":"
+    if lb_choice == 'LB1':
+        prefix_3 = " #1:"
+    elif lb_choice == 'LB2':
+        prefix_3 = " #2:"
+
+    return prefix_1 + prefix_2 + prefix_3
+
+def return_hero_stat(df0, hero_name, lb_choice="None", costume_choice="None"):
+    assert hero_name in df0.name.values
+    
+    display_cols_0 = ['image', 'name', 'color', 'star', 'class', 'speed',]
+    display_cols_1 = [] # ['power', 'attack', 'defense', 'health', ] --> to be select base one LB/Costume choice
+    display_cols_2 = ['Aether Power', 'source', 'family', 'types', 'skill', 'effects']
+
+    prefix = get_prefix(lb_choice, costume_choice)
+
+    display_cols_1.append(f'{prefix} Power')
+    display_cols_1.append(f'{prefix} Attack')
+    display_cols_1.append(f'{prefix} Defense')
+    display_cols_1.append(f'{prefix} Health')
+    
+    display_cols_all = display_cols_0 + display_cols_1 + display_cols_2
+    df_ret = df0[df0.name == hero_name][display_cols_all]
+
+    df_ret = df_ret.rename(columns={f'{prefix} Power':'power',
+                    f'{prefix} Attack':'attack',
+                    f'{prefix} Defense':'defense',
+                    f'{prefix} Health':'health'})
+    return df_ret
+
 #########################################
 ## Load the main file (TODO: caching)=
 st.set_page_config(layout="wide")
-
+st.header(f'HeroPlan Explorer')
+st.write('Powered by Heroplan.io : Thanks E&P community for continually update hero data.')
 
 df = pd.read_csv('heroes_ep.csv')
 class_values = ['None'] + list(df['class'].unique()) 
@@ -95,75 +160,108 @@ color_values = ['None'] + list(df['color'].unique())
 speed_values = ['None'] + list(df['speed'].unique())
 source_values = ['None'] + list(df['source'].unique())
 
-# defense_values = ['None'] + list(df['defense'].unique())
-# attack_values = ['None'] + list(df['attack'].unique())
-# health_values = ['None'] + list(df['health'].unique())
+#########################################
+## Select Main Program
+
+with st.sidebar:
+    genre = st.radio(
+    "Choose how to explore heroes",
+    [":rainbow[Heroes Explorer]", "***LB/CB Hero Stat*** :movie_camera:"],
+    captions = ["Filter only heroes with certain properties", "Co-powered by Elioty33's DataVault"])
 
 #########################################
-## Select options
-## TODO: family, costume
-
-# with st.sidebar:
-with st.expander("Filter Options"):
-    name_option = st.text_input(label="Name:", value="")
-    star_option = st.selectbox(label='Star:', options=star_values, index=0)
-    color_option = st.selectbox(label='Color:', options=color_values, index=0)
-    speed_option = st.selectbox(label='Speed:', options=speed_values, index=0)
-    class_option = st.selectbox(label='Class:', options=class_values, index=0)
-    source_option = st.selectbox(label='Origin:', options=source_values, index=0)
+## Program 1
+if genre == ':rainbow[Heroes Explorer]':
     
-    defense_option = st.text_input(label="Defense:", value="0")
-    attack_option = st.text_input(label="Attack:", value="0")
-    health_option = st.text_input(label="Health:", value="0")
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.header("Standard Filters:")
+        st.write("Tips: filter costume by typing ' C' or 'C2' in the Name box")
+        with st.expander("Filter Options"):
+            name_option = st.text_input(label="Name:", value="")
+            star_option = st.selectbox(label='Star:', options=star_values, index=0)
+            color_option = st.selectbox(label='Color:', options=color_values, index=0)
+            speed_option = st.selectbox(label='Speed:', options=speed_values, index=0)
+            class_option = st.selectbox(label='Class:', options=class_values, index=0)
+            source_option = st.selectbox(label='Origin:', options=source_values, index=0)
     
-    special_type_option = st.text_input(label="SpecialSkill Category", value="Hit 3")
-    special_text_option = st.text_input(label="SpecialSkill Text", value="Dispel")
-
-    st.title('Sorted By (or directly click at the column name)')
-    sort_option = st.selectbox(label='Sort by', options=display_cols[1:], index=5) # default is power
+            special_type_option = st.text_input(label="SpecialSkill Category", value="Hit 3")
+            special_text_option = st.text_input(label="SpecialSkill Text", value="Dispel")
+    with col2:
+        st.header("Stat Filters")
+        with st.expander("Stat Options"):   
+            defense_option = st.text_input(label="Defense:", value="0")
+            attack_option = st.text_input(label="Attack:", value="0")
+            health_option = st.text_input(label="Health:", value="0")
+    with col3:
+        st.header("Sorted By (or directly click at the column name)")
+        sort_option = st.selectbox(label='Sort by', options=display_cols[1:], index=5) # default is power
     
-idx_all = []
-
-if name_option != '':
-    idx_all.append(filter_by_1col(df, 'name', name_option, exact_flag=False)) 
+    idx_all = []
 
-if star_option != 'None':
-    idx_all.append(filter_by_1col_num(df, 'star', star_option, oper_flag="eq"))    
+    if name_option != '':
+        idx_all.append(filter_by_1col(df, 'name', name_option, exact_flag=False)) 
 
-if speed_option != 'None':
-    idx_all.append(filter_by_1col(df, 'speed', speed_option, exact_flag=True))    
+    if star_option != 'None':
+        idx_all.append(filter_by_1col_num(df, 'star', star_option, oper_flag="eq"))    
 
-if color_option != 'None':
-    idx_all.append(filter_by_1col(df, 'color', color_option, exact_flag=False))    
+    if speed_option != 'None':
+        idx_all.append(filter_by_1col(df, 'speed', speed_option, exact_flag=True))    
 
-if class_option != 'None':
-    idx_all.append(filter_by_1col(df, 'class', class_option, exact_flag=False))    
+    if color_option != 'None':
+        idx_all.append(filter_by_1col(df, 'color', color_option, exact_flag=False))    
 
-if source_option != 'None':
-    idx_all.append(filter_by_1col(df, 'source', source_option, exact_flag=False))    
+    if class_option != 'None':
+        idx_all.append(filter_by_1col(df, 'class', class_option, exact_flag=False))    
 
-if defense_option != "0":
-    defense_option = int(defense_option)
-    idx_all.append(filter_by_1col_num(df, 'defense', defense_option, oper_flag="ge"))   
+    if source_option != 'None':
+        idx_all.append(filter_by_1col(df, 'source', source_option, exact_flag=False))    
 
-if attack_option != "0":
-    attack_option = int(attack_option)
-    idx_all.append(filter_by_1col_num(df, 'attack', attack_option, oper_flag="ge"))   
+    if defense_option != "0":
+        defense_option = int(defense_option)
+        idx_all.append(filter_by_1col_num(df, 'defense', defense_option, oper_flag="ge"))   
 
-if health_option != "0":
-    health_option = int(health_option)
-    idx_all.append(filter_by_1col_num(df, 'health', health_option, oper_flag="ge"))   
+    if attack_option != "0":
+        attack_option = int(attack_option)
+        idx_all.append(filter_by_1col_num(df, 'attack', attack_option, oper_flag="ge"))   
 
+    if health_option != "0":
+        health_option = int(health_option)
+        idx_all.append(filter_by_1col_num(df, 'health', health_option, oper_flag="ge"))   
 
-if special_type_option  != '':
-    idx_all.append(filter_by_1col(df, 'types', special_type_option, exact_flag=False))    
+    if special_type_option  != '':
+        idx_all.append(filter_by_1col(df, 'types', special_type_option, exact_flag=False))    
 
-if special_text_option != '':
-    idx_all.append(filter_by_1col(df, 'effects', special_text_option, exact_flag=False))    
+    if special_text_option != '':
+        idx_all.append(filter_by_1col(df, 'effects', special_text_option, exact_flag=False))    
     
+    #########################################
+    st.header(f'Updated: Oct 23, 2023 -- Total heroes = {len(df)}')
+
+    df2 = df[np.all(idx_all,axis=0)]
+
+    display_heroes_from_df(df2.sort_values(sort_option, ascending=False))
 #########################################
-st.title(f'Updated: Oct 23, 2023 -- Total heroes = {len(df)}')
+## Program 2
+else:
+    df_extra = pd.read_csv("heroes_ep_extra.csv")
+    
+    st.header("Analyze Hero LB/CB Stat (without Emblem)")
+    st.write("HeroPlan and DataVault are combined here. Thanks ***@Elioty33*** for his DataVault contribution")
+    st.write(f"Currently, there are {len(df_extra)} heroes having both data on HeroPlan and DataVault.")
+    st.write(f"We don't have emblem calculator here, you can go heroplan.io to do the job.")
+    st.write(f"***Heuristically*** Choose Sword-path can increase att 100-150, def 50-100, hp ~100 (reverse att-def for shield path)")
+    st.write(f"Choose HP-path can increase att 50-100, def 50-100, hp ~200")
+    
+
+    name_values = sorted(list(df_extra['name'].values))
+    name_choice = st.selectbox(label='Hero Name:', options=name_values, index=0)
+
+    lb_list = ['None', 'LB1', 'LB2']
+    costume_list = return_costume_list(df_extra, name_choice)
 
-df2 = df[np.all(idx_all,axis=0)]
+    lb_choice = st.selectbox(label='Limit Break:', options=lb_list, index=0)
+    costume_choice = st.selectbox(label='Costume:', options=costume_list, index=0)
 
-display_heroes_from_df(df2.sort_values(sort_option, ascending=False))
+    df_ret = return_hero_stat(df_extra, name_choice, lb_choice=lb_choice, costume_choice=costume_choice)
+    display_heroes_from_df(df_ret,display_cols=df_ret.columns[:-2]) # display all except special-skill text