fixes provided

apps/dashboard_app/charts/constants.py

@@ -11,6 +11,7 @@
     "strk": "STRK",
 }
 
+
 @dataclass
 class ChartsHeaders:
     low_health_factor_loans: str = "Loans with low health factor"

apps/dashboard_app/charts/main.py

@@ -1,30 +1,37 @@
 """
 This module defines the Dashboard class for rendering a DeRisk dashboard using Streamlit.
 """
-import plotly
 import numpy as np
 import pandas as pd
+import plotly
 import streamlit as st
-from datetime import datetime, UTC
 from data_handler.handlers.loan_states.abstractions import State
 from shared.helpers import (
+    add_leading_zeros,
     extract_token_addresses,
     fetch_token_symbols_from_set_of_loan_addresses,
     update_loan_data_with_symbols,
-    add_leading_zeros
 )
 
-from helpers.settings import COLLATERAL_TOKENS, DEBT_TOKENS, STABLECOIN_BUNDLE_NAME, TOKEN_SETTINGS
+from helpers.settings import (
+    COLLATERAL_TOKENS,
+    DEBT_TOKENS,
+    STABLECOIN_BUNDLE_NAME,
+    TOKEN_SETTINGS,
+)
 
 from .constants import ChartsHeaders, CommonValues
-from .main_chart_figure import get_main_chart_figure, get_specific_loan_usd_amounts, get_bar_chart_figures
+from .main_chart_figure import (
+    get_bar_chart_figures,
+    get_main_chart_figure,
+    get_specific_loan_usd_amounts,
+)
 from .utils import (
     get_protocol_data_mappings,
+    infer_protocol_name,
     process_liquidity,
     transform_loans_data,
     transform_main_chart_data,
-    infer_protocol_name,
-
 )
 
 
@@ -39,7 +46,15 @@ class Dashboard:
         # "Nostra Mainnet",
     ]
 
-    def __init__(self, state: State, general_stats: dict, supply_stats: dict, collateral_stats: dict, debt_stats: dict, utilization_stats: dict):
+    def __init__(
+        self,
+        state: State,
+        general_stats: dict,
+        supply_stats: dict,
+        collateral_stats: dict,
+        debt_stats: dict,
+        utilization_stats: dict,
+    ):
         """
         Initialize the dashboard.
         """
@@ -167,12 +182,17 @@ def load_loans_with_low_health_factor_chart(self):
                 label="Select range of USD borrowings",
                 min_value=0,
                 max_value=int(loans_data[CommonValues.debt_usd.value].max()),
-                value=(0, int(loans_data[CommonValues.debt_usd.value].max()) or 1),  # FIXME remove 1
+                value=(
+                    0,
+                    int(loans_data[CommonValues.debt_usd.value].max()) or 1,
+                ),  # FIXME remove 1
             )
 
         st.dataframe(
             loans_data[
-                (loans_data[CommonValues.health_factor.value] > 0)  # TODO: debug the negative HFs
+                (
+                    loans_data[CommonValues.health_factor.value] > 0
+                )  # TODO: debug the negative HFs
                 & loans_data[CommonValues.debt_usd.value].between(
                     debt_usd_lower_bound, debt_usd_upper_bound
                 )
@@ -203,8 +223,13 @@ def load_top_loans_chart(self):
             st.subheader("Sorted by collateral")
             st.dataframe(
                 loans_data[
-                    (loans_data[CommonValues.health_factor.value] > 1)  # TODO: debug the negative HFs
-                    & (loans_data[CommonValues.standardized_health_factor.value] != float("inf"))
+                    (
+                        loans_data[CommonValues.health_factor.value] > 1
+                    )  # TODO: debug the negative HFs
+                    & (
+                        loans_data[CommonValues.standardized_health_factor.value]
+                        != float("inf")
+                    )
                 ]
                 .sort_values(CommonValues.collateral_usd.value, ascending=False)
                 .iloc[:20],
@@ -216,7 +241,8 @@ def load_top_loans_chart(self):
                 loans_data[
                     (loans_data[CommonValues.health_factor.value] > 1)
                     & (
-                        loans_data[CommonValues.standardized_health_factor.value] != float("inf")
+                        loans_data[CommonValues.standardized_health_factor.value]
+                        != float("inf")
                     )  # TODO: debug the negative HFs
                 ]
                 .sort_values(CommonValues.debt_usd.value, ascending=False)
@@ -277,13 +303,12 @@ def load_detail_loan_chart(self):
             protocol = infer_protocol_name(protocol, valid_protocols)
 
         loan = loans_data_main.loc[
-            (loans_data[CommonValues.user.value] == user) & (loans_data[CommonValues.protocol.value] == protocol),
+            (loans_data[CommonValues.user.value] == user)
+            & (loans_data[CommonValues.protocol.value] == protocol),
         ]
 
         if loan.empty:
-            st.warning(
-                f"No loan found for user = {user} and protocol = {protocol}."
-            )
+            st.warning(f"No loan found for user = {user} and protocol = {protocol}.")
         else:
             (
                 collateral_usd_amounts,
@@ -341,6 +366,8 @@ def load_comparison_lending_protocols_chart(self):
         for column, token_1, token_2 in zip(columns, tokens[:4], tokens[4:]):
             with column:
                 for token in [token_1, token_2]:
+                    if token == "WBTC":
+                        token = "wBTC"
                     figure = plotly.express.pie(
                         self.collateral_stats.reset_index(),
                         values=f"{token} collateral",
@@ -352,17 +379,19 @@ def load_comparison_lending_protocols_chart(self):
                 for token in [token_1, token_2]:
                     figure = plotly.express.pie(
                         self.debt_stats.reset_index(),
-                        values=f"{token} debt",
-                        names=CommonValues.protocol.value,
+                        values=f"{token} debt".lower(),
+                        names=CommonValues.protocol.value.lower(),
                         title=f"{token} debt",
                         color_discrete_sequence=plotly.express.colors.sequential.Greens_r,
                     )
                     st.plotly_chart(figure, True)
                 for token in [token_1, token_2]:
+                    if "dai" in token.lower():
+                        continue
                     figure = plotly.express.pie(
                         self.supply_stats.reset_index(),
-                        values=f"{token} supply",
-                        names=CommonValues.protocol.value,
+                        values=f"{token} supply".lower(),
+                        names=CommonValues.protocol.value.lower(),
                         title=f"{token} supply",
                         color_discrete_sequence=plotly.express.colors.sequential.Blues_r,
                     )
@@ -389,7 +418,7 @@ def run(self):
         # Load sidebar with protocol settings
         self.load_sidebar()
         self.load_main_chart()
-        # self.load_loans_with_low_health_factor_chart()
-        # self.load_top_loans_chart()
-        # self.load_detail_loan_chart()
+        self.load_loans_with_low_health_factor_chart()
+        self.load_top_loans_chart()
+        self.load_detail_loan_chart()
         self.load_comparison_lending_protocols_chart()

apps/dashboard_app/charts/main_chart_figure.py

@@ -4,12 +4,13 @@
 
 import math
 from decimal import Decimal
+
 import pandas as pd
 import plotly.express
 import plotly.graph_objs
 from shared.amms import SwapAmm
-from shared.state import State
 from shared.custom_types import Prices
+from shared.state import State
 
 from helpers.settings import TOKEN_SETTINGS
 from helpers.tools import (
@@ -18,6 +19,7 @@
     get_prices,
     get_underlying_address,
 )
+
 from .constants import SUPPLY_STATS_TOKEN_SYMBOLS_MAPPING
 
 AMMS = ("10kSwap", "MySwap", "SithSwap", "JediSwap")
@@ -213,20 +215,36 @@ def get_bar_chart_figures(
     for column in supply_stats.columns:
         if "protocol" in column.lower():
             bar_chart_supply_stats[column] = supply_stats[column][0]
-            bar_chart_collateral_stats[column] = collateral_stats[column][0]
+            bar_chart_collateral_stats[column] = collateral_stats[column.capitalize()][
+                0
+            ]
             bar_chart_debt_stats[column] = debt_stats[column][0]
             continue
         elif "total" in column.lower():
             continue
         underlying_symbol = column.split(" ")[0]
-        underlying_address = underlying_symbols_to_addresses[SUPPLY_STATS_TOKEN_SYMBOLS_MAPPING[underlying_symbol]]
-        bar_chart_supply_stats[underlying_symbol] = supply_stats[column].loc[0] * Decimal(prices[underlying_address])
+        underlying_address = underlying_symbols_to_addresses[
+            SUPPLY_STATS_TOKEN_SYMBOLS_MAPPING[underlying_symbol]
+        ]
+        bar_chart_supply_stats[underlying_symbol] = supply_stats[column].loc[
+            0
+        ] * Decimal(prices[underlying_address])
         # handling the specific case with WBTC token
         if underlying_symbol == "wbtc":
-            bar_chart_collateral_stats[underlying_symbol] = (collateral_stats["wBTC collateral"].loc[0] * prices[underlying_address])
+            bar_chart_collateral_stats[underlying_symbol] = (
+                collateral_stats["wBTC collateral"].loc[0] * prices[underlying_address]
+            )
         else:
-            bar_chart_collateral_stats[underlying_symbol] = (collateral_stats[SUPPLY_STATS_TOKEN_SYMBOLS_MAPPING[underlying_symbol] + " collateral"].loc[0] * prices[underlying_address])
-        bar_chart_debt_stats[underlying_symbol] = (debt_stats[underlying_symbol + " debt"].loc[0] * prices[underlying_address])
+            bar_chart_collateral_stats[underlying_symbol] = (
+                collateral_stats[
+                    SUPPLY_STATS_TOKEN_SYMBOLS_MAPPING[underlying_symbol]
+                    + " collateral"
+                ].loc[0]
+                * prices[underlying_address]
+            )
+        bar_chart_debt_stats[underlying_symbol] = (
+            debt_stats[underlying_symbol + " debt"].loc[0] * prices[underlying_address]
+        )
 
     bar_chart_supply_stats = bar_chart_supply_stats.T
     bar_chart_collateral_stats = bar_chart_collateral_stats.T
@@ -237,7 +255,7 @@ def get_bar_chart_figures(
             plotly.graph_objs.Bar(
                 name="zkLend",
                 x=bar_chart_supply_stats.index,
-                y=bar_chart_supply_stats["zkLend"],
+                y=bar_chart_supply_stats[0],
                 marker=plotly.graph_objs.bar.Marker(color="#fff7bc"),
             ),
             # TODO: add functionality for other protocols
@@ -249,7 +267,7 @@ def get_bar_chart_figures(
             plotly.graph_objs.Bar(
                 name="zkLend",
                 x=bar_chart_collateral_stats.index,
-                y=bar_chart_collateral_stats["zkLend"],
+                y=bar_chart_collateral_stats[0],
                 marker=plotly.graph_objs.bar.Marker(color="#fff7bc"),
             ),
             # TODO: add functionality for other protocols
@@ -261,7 +279,7 @@ def get_bar_chart_figures(
             plotly.graph_objs.Bar(
                 name="zkLend",
                 x=bar_chart_debt_stats.index,
-                y=bar_chart_debt_stats["zkLend"],
+                y=bar_chart_debt_stats[0],
                 marker=plotly.graph_objs.bar.Marker(color="#fff7bc"),
             ),
             # TODO: add functionality for other protocols

apps/dashboard_app/charts/utils.py

@@ -1,8 +1,8 @@
 """
 This moudel process and transform liquidity, loan, and chart data for protocols.
 """
-import difflib
 import asyncio
+import difflib
 import logging
 import math
 import time
@@ -306,6 +306,7 @@ def transform_main_chart_data(
 
     return main_chart_data
 
+
 def infer_protocol_name(input_protocol: str, valid_protocols: list[str]) -> str:
     """Find the closest matching protocol name from a list of valid protocols using fuzzy matching.