Update src/data_loader.py

src/data_loader.py

@@ -2,52 +2,76 @@
 import streamlit as st
 import pandas as pd
 import os
-from datasets import load_dataset
-import kagglehub
 
-@st.cache_data(show_spinner=False)
-def load_csv(file):
-    return pd.read_csv(file)
+# Try importing optional dependencies
+try:
+    from datasets import load_dataset
+    import kagglehub
+except ImportError:
+    pass # Handle usage inside functions
 
-@st.cache_data(show_spinner=False)
-def load_hf(path, split):
-    return pd.DataFrame(load_dataset(path, split=split))
+@st.cache_data(show_spinner="Loading CSV file...", ttl=3600)
+def load_csv(file) -> pd.DataFrame:
+    try:
+        df = pd.read_csv(file)
+        df.reset_index(drop=True, inplace=True)
+        return df
+    except Exception as e:
+        st.error(f"Failed to load CSV: {e}")
+        return None
 
-@st.cache_data(show_spinner=False)
-def load_kaggle(kaggle_path, file_name):
-    path = kagglehub.dataset_download(kaggle_path)
-    return pd.read_csv(os.path.join(path, file_name))
+@st.cache_data(show_spinner="Downloading from Hugging Face...", ttl=3600)
+def load_hf(path: str, split: str) -> pd.DataFrame:
+    try:
+        ds = load_dataset(path, split=split)
+        df = pd.DataFrame(ds)
+        return df
+    except Exception as e:
+        st.error(f"Failed to load HF dataset: {e}")
+        return None
 
-def dataset_sidebar():
-    st.sidebar.header("1️⃣ Dataset Loader")
+@st.cache_data(show_spinner="Downloading from Kaggle...", ttl=3600)
+def load_kaggle(kaggle_path: str, file_name: str) -> pd.DataFrame:
+    try:
+        path = kagglehub.dataset_download(kaggle_path)
+        full_path = os.path.join(path, file_name)
+        return pd.read_csv(full_path)
+    except Exception as e:
+        st.error(f"Failed to load Kaggle dataset: {e}")
+        return None
 
+def dataset_sidebar():
+    st.sidebar.header("1️⃣ Data Ingestion")
+    
     source = st.sidebar.selectbox(
-        "Data Source",
-        ["Upload CSV", "Hugging Face", "Kaggle"]
+        "Source Type",
+        ["Upload CSV", "Hugging Face", "Kaggle"],
+        help="Select the source of your dataset."
     )
 
     df = None
 
     if source == "Upload CSV":
-        file = st.sidebar.file_uploader("Upload CSV", type=["csv"])
-        if file and st.sidebar.button("Load CSV"):
+        file = st.sidebar.file_uploader("Drop CSV Here", type=["csv"])
+        if file:
             df = load_csv(file)
 
     elif source == "Hugging Face":
-        path = st.sidebar.text_input("Dataset name", "iris")
-        split = st.sidebar.text_input("Split", "train")
-        if st.sidebar.button("Load HF Dataset"):
+        path = st.sidebar.text_input("Dataset Path (e.g., 'iris')", "iris")
+        split = st.sidebar.text_input("Split (e.g., 'train')", "train")
+        if st.sidebar.button("Fetch HF Dataset"):
             df = load_hf(path, split)
 
     elif source == "Kaggle":
-        kp = st.sidebar.text_input("Kaggle path")
-        fn = st.sidebar.text_input("File name")
-        if st.sidebar.button("Load Kaggle Dataset"):
+        kp = st.sidebar.text_input("Kaggle Path (user/dataset)")
+        fn = st.sidebar.text_input("CSV Filename inside dataset")
+        if st.sidebar.button("Fetch Kaggle Dataset"):
             df = load_kaggle(kp, fn)
 
+    # State Update Logic
     if df is not None:
-        st.session_state.original_df = df
-        st.session_state.processed_df = df.copy()
-        st.success("Dataset loaded")
-
-    return df
+        if st.session_state.original_df is None or not df.equals(st.session_state.original_df):
+            st.session_state.original_df = df.copy()
+            # Initialize processed_df as a copy of original
+            st.session_state.processed_df = df.copy()
+            st.sidebar.success(f"Loaded: {df.shape[0]} rows, {df.shape[1]} cols")