#57 Fix: Bugs in the Streamlit UI

README.md

@@ -8,8 +8,7 @@
     <img src="data/assets/demo-snippet.png" alt="DORA Demo" width="700"/>
 </p>
 
-<em align="center">
-An interactive command-line tool to automate Exploratory Data Analysis (EDA) and generate beautiful, insightful reports in seconds.
+An interactive power-tool to automate Exploratory Data Analysis (EDA) and generate beautiful, insightful reports in seconds.
 </em>
 
 <p align="center">

app.py

@@ -135,18 +135,33 @@ def load_kaggle_data(kaggle_input):
             else:
                 dataset_id = kaggle_input
 
-            # Download using existing handler
-            file_path = KaggleHandler.download_dataset(dataset_id)
+            # Fetch all supported files
+            files = KaggleHandler.download_files(dataset_id)
+
+            # Store found files in session state so we can let the user pick one if needed
+            st.session_state.kaggle_files = files
+            st.session_state.kaggle_dataset_id = dataset_id
 
-            # Load data
-            df = read_data(file_path)
-            st.session_state.df = df
-            st.session_state.input_source = dataset_id
-            st.success(f"Successfully loaded data from '{dataset_id}'")
+            # If there's only one file, load it immediately
+            if len(files) == 1:
+               _load_specific_kaggle_file(files[0], dataset_id)
 
     except Exception as e:
         st.error(f"Error processing Kaggle dataset: {e}")
 
+def _load_specific_kaggle_file(file_path, dataset_id):
+    """Helper to load a specific file from a Kaggle dataset."""
+    try:
+        with st.spinner(f"Loading {file_path.name}..."):
+            df = read_data(file_path)
+            st.session_state.df = df
+            st.session_state.input_source = f"{dataset_id}/{file_path.name}"
+            st.success(f"Successfully loaded '{file_path.name}' from '{dataset_id}'")
+            # Clear the file list selection state once loaded, if you prefer
+            # st.session_state.kaggle_files = None 
+    except Exception as e:
+        st.error(f"Error loading file: {e}")
+
 
 def render_ingestion():
     """Render the data ingestion section (Local File & Kaggle Tabs)."""
@@ -173,6 +188,33 @@ def render_ingestion():
                 load_kaggle_data(kaggle_input)
             else:
                 st.warning("Please enter a valid Dataset ID or URL.")
+
+        # Check if we have multiple files to choose from
+        if "kaggle_files" in st.session_state and st.session_state.kaggle_files:
+            files = st.session_state.kaggle_files
+            if len(files) > 1:
+                st.info(f"Found {len(files)} files. Please select one:")
+
+                # Create a mapping of full path -> file object to ensure uniqueness
+                file_mapping = {str(f): f for f in files}
+
+                def extract_display_name(path_str):
+                    """Format the display name to be shorter if possible"""
+                    f = file_mapping[path_str]
+                    return f"{f.parent.name}/{f.name}"
+
+                selected_file_key = st.selectbox(
+                    "Select file", 
+                    options=list(file_mapping.keys()), 
+                    format_func=extract_display_name,
+                    key="kaggle_file_select"
+                )
+
+                if st.button("Load Selected File", key="btn_kaggle_multiload"):
+                    # Find the path for the selected file using the mapping
+                    selected_path = file_mapping.get(selected_file_key)
+                    if selected_path:
+                         _load_specific_kaggle_file(selected_path, st.session_state.kaggle_dataset_id)
 
 
 def render_preview():

src/dora/kaggle.py

@@ -50,12 +50,12 @@ def extract_dataset_id(input_str: str) -> str:
         return input_str
 
     @staticmethod
-    def download_dataset(dataset_id: str) -> Path:
+    def download_files(dataset_id: str) -> list[Path]:
         """
-        Download a Kaggle dataset from kagglehub and return the path to the downloaded file.
-
-        :param dataset_id: The 'owner/dataset-name' identifier of the dataset to download.
-        :return: The path to the downloaded file.
+        Download a Kaggle dataset and return a list of all supported files.
+        
+        :param dataset_id: The 'owner/dataset-name' identifier.
+        :return: List of Path objects for supported files.
         """
         logging.info("Downloading dataset %s", dataset_id)
         try:
@@ -74,6 +74,19 @@ def download_dataset(dataset_id: str) -> Path:
 
         if not files:
             raise ValueError("No supported files found in the downloaded dataset.")
+
+        return files
+
+    @staticmethod
+    def download_dataset(dataset_id: str) -> Path:
+        """
+        Download a Kaggle dataset from kagglehub and return the path to the downloaded file.
+        If multiple files are present, it prompts the user to select one (CLI mode).
+
+        :param dataset_id: The 'owner/dataset-name' identifier of the dataset to download.
+        :return: The path to the downloaded file.
+        """
+        files = KaggleHandler.download_files(dataset_id)
 
         if len(files) == 1:
             return files[0]

src/dora/plots/multivariate.py

@@ -11,6 +11,7 @@
 matplotlib.use("Agg")
 
 import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
 import seaborn as sns
 
@@ -48,8 +49,9 @@ def generate_plots(
 
     plt.figure(figsize=(12, 10))
     corr = df_numeric.corr()
+    mask = np.triu(np.ones_like(corr, dtype=bool))
     cmap = sns.diverging_palette(230, 20, as_cmap=True)
-    sns.heatmap(corr, annot=True, fmt=".2f", cmap=cmap, linewidths=0.5)
+    sns.heatmap(corr, mask=mask, annot=True, fmt=".2f", cmap=cmap, linewidths=0.5)
     plt.title(
         "Correlation Matrix of Numerical Features",
         loc="left",