hotfixes

interface/dashboard.py

@@ -1,10 +1,10 @@
 import pandas as pd
 import streamlit as st
 
-from core.utils import get_data_file_path
 from core.utils.data_loader import OPTIMIZED_DTYPES
 
 
+@st.cache_data
 def load_dataset(file_path: str) -> pd.DataFrame:
     try:
         return pd.read_csv(file_path, dtype=OPTIMIZED_DTYPES)
@@ -25,7 +25,7 @@ class Dashboard:
 
         # Load basic statistics
         try:
-            data_path = get_data_file_path(self.config.data.output_files["featured"], self.config)
+            data_path = self.config.paths.get_data_path(self.config.data.output_files["featured"])
             if data_path.exists():
                 df = load_dataset(str(data_path))
 

interface/data_overview.py

@@ -4,10 +4,10 @@ import pandas as pd
 import plotly.express as px
 import streamlit as st
 
-from core.utils import get_data_file_path
 from core.utils.data_loader import OPTIMIZED_DTYPES
 
 
+@st.cache_data
 def load_dataset(file_path: str) -> pd.DataFrame:
     try:
         return pd.read_csv(file_path, dtype=OPTIMIZED_DTYPES)
@@ -31,7 +31,7 @@ class DataOverview:
         }
 
         selected_file = st.selectbox("Select Dataset", list(data_files.keys()))
-        file_path = get_data_file_path(data_files[selected_file], self.config)
+        file_path = self.config.paths.get_data_path(data_files[selected_file])
 
         if not file_path.exists():
             st.warning(f"Dataset not found: {file_path}")

interface/data_processing.py

@@ -6,6 +6,7 @@ from core.utils.data_loader import OPTIMIZED_DTYPES
 from interface.log_reader import LogReader
 
 
+@st.cache_data
 def load_dataset(file_path: str) -> pd.DataFrame:
     try:
         return pd.read_csv(file_path, dtype=OPTIMIZED_DTYPES)

interface/predictions.py

@@ -1,5 +1,3 @@
-"""Predictions interface for the Streamlit app"""
-
 from datetime import datetime
 from typing import Optional
 
@@ -8,7 +6,6 @@ import pandas as pd
 import plotly.express as px
 import streamlit as st
 
-from core.utils import get_data_file_path
 from core.utils.data_loader import OPTIMIZED_DTYPES
 from research.experiment.experiment_runner import ExperimentRunner
 from research.experiment.experiment_tracker import ExperimentTracker
@@ -268,7 +265,7 @@ class Predictions:
         }
 
         selected_dataset = st.selectbox("Select Dataset", list(dataset_options.keys()))
-        file_path = get_data_file_path(dataset_options[selected_dataset], self.config)
+        file_path = self.config.paths.get_data_path(dataset_options[selected_dataset])
 
         if not file_path.exists():
             st.warning(f"Dataset not found: {file_path}")