import streamlit as st import pandas as pd import plotly.io as pio import traceback import sys from datetime import datetime from data_preprocessing import preprocess_data from insights import generate_business_insights from dataset_overview import eda_analysis # Updated import from visualization import auto_visualizations from ml_pipeline import run_ml_pipeline from statistical_analysis import statistical_analysis from data_quality import quality_report from chatbot import data_chatbot # Set plotly template pio.templates.default = "plotly_white" # --------------------------------------- # PAGE CONFIG # --------------------------------------- st.set_page_config( page_title="AI Data Analyst Pro", layout="wide", page_icon="📊", initial_sidebar_state="expanded" ) # --------------------------------------- # CUSTOM ERROR HANDLER # --------------------------------------- class StreamlitExceptionHandler: """Custom exception handler for Streamlit""" @staticmethod def handle_exception(e, context="application"): """Handle exceptions with user-friendly messages""" error_type = type(e).__name__ error_msg = str(e) # Create user-friendly error message user_message = f""" ### ❌ An error occurred in the {context} **Error Type:** {error_type} **What happened:** {error_msg if error_msg else "An unexpected error occurred"} **Possible solutions:** """ # Add specific solutions based on error type if "MemoryError" in error_type: user_message += """ - Your dataset might be too large. Try uploading a smaller file. - Close other applications to free up memory. - Consider sampling your data before uploading. """ elif "KeyError" in error_type or "IndexError" in error_type: user_message += """ - The requested column or index doesn't exist in your dataset. - Check if you've selected valid columns for the operation. - Try refreshing the page and uploading your data again. """ elif "ValueError" in error_type: user_message += """ - The data values don't match the expected format. - Check for invalid values in your dataset (e.g., text in numeric columns). - Ensure your data types are correct for the selected operation. """ elif "TypeError" in error_type: user_message += """ - There's a mismatch in data types. - Check if you're mixing numeric and text data in operations. - Use the preprocessing tab to convert data types appropriately. """ elif "FileNotFoundError" in error_type: user_message += """ - The file couldn't be found. Please upload it again. - Check if the file path is correct. """ elif "PermissionError" in error_type: user_message += """ - Permission denied when accessing the file. - Make sure the file isn't open in another program. """ elif "pd.errors.EmptyDataError" in error_type: user_message += """ - The uploaded file is empty. - Please upload a file containing data. """ elif "pd.errors.ParserError" in error_type: user_message += """ - Couldn't parse the file. Check if it's a valid CSV or Excel file. - Ensure the file format matches the selected file type. """ else: user_message += """ - Try refreshing the page and uploading your data again. - Check if your data format is compatible with the operation. - If the problem persists, try with a smaller sample of your data. """ # Add technical details in an expander for debugging user_message += f""" **Technical Details:** """ return user_message # Initialize session state for error tracking if "error_log" not in st.session_state: st.session_state.error_log = [] if "last_successful_operation" not in st.session_state: st.session_state.last_successful_operation = None # --------------------------------------- # ADVANCED CSS WITH RESPONSIVE DESIGN # --------------------------------------- st.markdown(""" """, unsafe_allow_html=True) # --------------------------------------- # HEADER WITH ANIMATION # --------------------------------------- st.markdown("""
📊 AI Data Analyst Pro
Intelligent Data Analysis & Visualization Platform
""", unsafe_allow_html=True) # --------------------------------------- # SESSION STATE INITIALIZATION # --------------------------------------- if "data" not in st.session_state: st.session_state.data = None if "processed_data" not in st.session_state: st.session_state.processed_data = None if "uploaded_file_name" not in st.session_state: st.session_state.uploaded_file_name = None if "upload_error" not in st.session_state: st.session_state.upload_error = None if "data_loaded" not in st.session_state: st.session_state.data_loaded = False if "operation_status" not in st.session_state: st.session_state.operation_status = {} # --------------------------------------- # HELPER FUNCTIONS # --------------------------------------- def safe_dataframe_operation(func, df, *args, **kwargs): """Safely execute dataframe operations with error handling""" try: result = func(df, *args, **kwargs) st.session_state.last_successful_operation = func.__name__ return result, None except Exception as e: error_msg = StreamlitExceptionHandler.handle_exception(e, func.__name__) return None, error_msg def validate_dataset(df): """Validate dataset for common issues""" issues = [] if df.empty: issues.append("The dataset is empty") if df.shape[0] == 0: issues.append("No rows in the dataset") if df.shape[1] == 0: issues.append("No columns in the dataset") # Check for memory issues memory_usage = df.memory_usage(deep=True).sum() / 1024**3 # GB if memory_usage > 1: issues.append(f"Large dataset detected ({memory_usage:.2f} GB). Some operations may be slow.") # Check for mixed types for col in df.columns: if df[col].dtype == 'object': # Check if column has mixed types types = df[col].apply(type).unique() if len(types) > 1: issues.append(f"Column '{col}' has mixed data types: {types}") return issues def show_validation_warnings(issues): """Display validation warnings""" if issues: st.markdown("""
⚠ī¸ Data Quality Warnings:
""" + "
".join([f"â€ĸ {issue}" for issue in issues]) + """
""", unsafe_allow_html=True) # --------------------------------------- # SIDEBAR WITH ENHANCED NAVIGATION # --------------------------------------- with st.sidebar: st.markdown("### 🧭 Navigation") # Custom radio buttons styling page = st.radio( "Select Module", ["📤 Upload Dataset", "🛠ī¸ Preprocessing", "🔍 EDA", "📈 Visualization", "🤖 Machine Learning", "💡 Insights", "đŸ’Ŧ Chatbot", "📋 Data Quality", "📐 Statistical Analysis"], label_visibility="collapsed" ) st.markdown("---") # Dataset info in sidebar if st.session_state.data is not None: st.markdown("### 📂 Current Dataset") df = st.session_state.data col1, col2 = st.columns(2) with col1: st.metric("Rows", f"{df.shape[0]:,}") with col2: st.metric("Columns", df.shape[1]) # Show data quality indicator missing_pct = (df.isnull().sum().sum() / (df.shape[0] * df.shape[1])) * 100 if missing_pct == 0: st.success("✅ Data Quality: Excellent") elif missing_pct < 5: st.info(f"ℹī¸ Data Quality: Good ({missing_pct:.1f}% missing)") elif missing_pct < 20: st.warning(f"⚠ī¸ Data Quality: Fair ({missing_pct:.1f}% missing)") else: st.error(f"❌ Data Quality: Poor ({missing_pct:.1f}% missing)") # Quick actions st.markdown("### ⚡ Quick Actions") col1, col2 = st.columns(2) with col1: if st.button("🔄 Reset Data", use_container_width=True): st.session_state.data = None st.session_state.processed_data = None st.session_state.data_loaded = False st.rerun() with col2: if st.button("đŸ“Ĩ Download Sample", use_container_width=True): # Create sample data download sample_df = df.head(100) csv = sample_df.to_csv(index=False) st.download_button( label="Download Sample", data=csv, file_name="sample_data.csv", mime="text/csv" ) # Show operation history if st.session_state.operation_status: with st.expander("📋 Operation History"): for op, status in st.session_state.operation_status.items(): if status == "success": st.success(f"✅ {op}") elif status == "error": st.error(f"❌ {op}") else: st.info(f"âŗ {op}") else: st.info("👆 Upload a dataset to get started") # --------------------------------------- # MAIN CONTENT AREA # --------------------------------------- # Map page names to functions page_map = { "📤 Upload Dataset": "upload", "🛠ī¸ Preprocessing": "preprocess", "🔍 EDA": "eda", "📈 Visualization": "visualization", "🤖 Machine Learning": "ml", "💡 Insights": "insights", "đŸ’Ŧ Chatbot": "chatbot", "📋 Data Quality": "quality", "📐 Statistical Analysis": "statistical" } current_page = page_map[page] # --------------------------------------- # UPLOAD DATASET PAGE # --------------------------------------- if current_page == "upload": col1, col2, col3 = st.columns([1, 2, 1]) with col2: st.markdown('
', unsafe_allow_html=True) st.markdown("### 📂 Upload Your Dataset") # File uploader with size limit warning file = st.file_uploader( "Choose a CSV or Excel file", type=["csv", "xlsx"], help="Maximum recommended file size: 200MB. Larger files may cause performance issues." ) if file: try: # Check file size file_size = file.size / 1024**2 # MB if file_size > 200: st.warning(f"⚠ī¸ Large file detected ({file_size:.2f} MB). Processing may be slow.") with st.spinner("📂 Loading file..."): # Read file based on extension if file.name.endswith("csv"): # Try different encodings encodings = ['utf-8', 'latin-1', 'iso-8859-1', 'cp1252'] df = None for encoding in encodings: try: df = pd.read_csv(file, encoding=encoding) break except UnicodeDecodeError: continue if df is None: st.error("❌ Could not read CSV file with any common encoding.") st.stop() elif file.name.endswith(("xlsx", "xls")): try: df = pd.read_excel(file) except Exception as e: st.error(f"❌ Error reading Excel file: {str(e)}") st.info("💡 Try saving the file as CSV and uploading again.") st.stop() # Validate dataset issues = validate_dataset(df) show_validation_warnings(issues) if not issues or all("Large dataset" not in issue for issue in issues): # Store in session state st.session_state.data = df st.session_state.uploaded_file_name = file.name st.session_state.data_loaded = True st.session_state.upload_error = None # Show success message st.markdown("""
✅ Successfully loaded: {}
📊 Shape: {} rows × {} columns
""".format(file.name, df.shape[0], df.shape[1]), unsafe_allow_html=True) # File statistics st.markdown("### 📊 File Statistics") col1, col2, col3 = st.columns(3) with col1: st.metric("Total Rows", f"{df.shape[0]:,}") with col2: st.metric("Total Columns", df.shape[1]) with col3: memory = df.memory_usage(deep=True).sum() / 1024**2 st.metric("Memory Usage", f"{memory:.2f} MB") # Data preview with scroll st.markdown("### 👁ī¸ Data Preview") st.dataframe( df.head(10), use_container_width=True, height=300 ) # Column info with sorting st.markdown("### 📋 Column Information") col_info = pd.DataFrame({ 'Column': df.columns, 'Type': df.dtypes.astype(str), 'Non-Null Count': df.count().values, 'Null Count': df.isnull().sum().values, 'Null %': (df.isnull().sum().values / len(df) * 100).round(2), 'Unique Values': [df[col].nunique() for col in df.columns] }) # Sort by null count col_info = col_info.sort_values('Null %', ascending=False) st.dataframe( col_info.style.background_gradient(subset=['Null %'], cmap='YlOrRd'), use_container_width=True ) # Quick stats st.markdown("### 📈 Quick Statistics") numeric_cols = df.select_dtypes(include=['number']).columns if len(numeric_cols) > 0: st.dataframe( df[numeric_cols].describe(), use_container_width=True ) # Navigation buttons st.markdown("### 🚀 Next Steps") col1, col2, col3 = st.columns(3) with col1: if st.button("🛠ī¸ Go to Preprocessing", use_container_width=True): st.session_state.page = "🛠ī¸ Preprocessing" st.rerun() with col2: if st.button("📊 Go to EDA", use_container_width=True): st.session_state.page = "📊 EDA" st.rerun() with col3: if st.button("📈 Go to Visualization", use_container_width=True): st.session_state.page = "📈 Visualization" st.rerun() except pd.errors.EmptyDataError: st.error("❌ The uploaded file is empty. Please upload a file with data.") except pd.errors.ParserError as e: st.error(f"❌ Error parsing file: {str(e)}") st.info("💡 Check if your CSV file has consistent delimiters and quoting.") except MemoryError: st.error("❌ Out of memory! The file is too large to process.") st.info("💡 Try uploading a smaller file or sampling your data first.") except Exception as e: error_msg = StreamlitExceptionHandler.handle_exception(e, "file upload") st.markdown(f'
{error_msg}
', unsafe_allow_html=True) # Log error st.session_state.error_log.append({ 'timestamp': datetime.now(), 'error': str(e), 'traceback': traceback.format_exc() }) st.markdown('
', unsafe_allow_html=True) # Sample data option with st.expander("🔄 Or use sample data"): st.markdown("Don't have a dataset? Try our sample data:") if st.button("Load Sample Dataset", use_container_width=True): try: from utils import create_sample_dataset sample_df = create_sample_dataset() st.session_state.data = sample_df st.session_state.uploaded_file_name = "sample_dataset.csv" st.session_state.data_loaded = True st.success("✅ Sample dataset loaded successfully!") st.rerun() except Exception as e: st.error(f"❌ Error loading sample data: {str(e)}") # --------------------------------------- # PREPROCESSING PAGE # --------------------------------------- elif current_page == "preprocess": try: if st.session_state.data is not None: df = st.session_state.data # Validate data before preprocessing issues = validate_dataset(df) if issues: show_validation_warnings(issues) # Run preprocessing with error handling with st.spinner("🔄 Preprocessing data..."): processed_df, error = safe_dataframe_operation(preprocess_data, df) if error: st.markdown(f'
{error}
', unsafe_allow_html=True) st.session_state.operation_status['Preprocessing'] = 'error' else: st.session_state.processed_data = processed_df st.session_state.operation_status['Preprocessing'] = 'success' # Show success message st.markdown("""
✅ Preprocessing completed successfully!
You can now proceed to analysis or visualization.
""", unsafe_allow_html=True) else: st.warning("⚠ī¸ Please upload a dataset first in the Upload section") except Exception as e: error_msg = StreamlitExceptionHandler.handle_exception(e, "preprocessing") st.markdown(f'
{error_msg}
', unsafe_allow_html=True) # --------------------------------------- # EDA PAGE # --------------------------------------- elif current_page == "eda": try: if st.session_state.data is not None: df = st.session_state.data # Validate data issues = validate_dataset(df) if issues: show_validation_warnings(issues) # Run EDA with error handling with st.spinner("🔍 Performing Exploratory Data Analysis..."): result, error = safe_dataframe_operation(eda_analysis, df) if error: st.markdown(f'
{error}
', unsafe_allow_html=True) st.session_state.operation_status['EDA'] = 'error' else: st.session_state.operation_status['EDA'] = 'success' else: st.warning("⚠ī¸ Please upload a dataset first in the Upload section") except Exception as e: error_msg = StreamlitExceptionHandler.handle_exception(e, "EDA") st.markdown(f'
{error_msg}
', unsafe_allow_html=True) # --------------------------------------- # VISUALIZATION PAGE # --------------------------------------- elif current_page == "visualization": try: if st.session_state.data is not None: df = st.session_state.data # Validate data issues = validate_dataset(df) if issues: show_validation_warnings(issues) # Run visualization with error handling with st.spinner("📊 Generating visualizations..."): result, error = safe_dataframe_operation(auto_visualizations, df) if error: st.markdown(f'
{error}
', unsafe_allow_html=True) st.session_state.operation_status['Visualization'] = 'error' else: st.session_state.operation_status['Visualization'] = 'success' else: st.warning("⚠ī¸ Please upload a dataset first in the Upload section") except Exception as e: error_msg = StreamlitExceptionHandler.handle_exception(e, "visualization") st.markdown(f'
{error_msg}
', unsafe_allow_html=True) # --------------------------------------- # MACHINE LEARNING PAGE # --------------------------------------- elif current_page == "ml": try: if st.session_state.data is not None: data_to_use = st.session_state.processed_data if st.session_state.processed_data is not None else st.session_state.data # Validate data for ML if data_to_use.shape[0] < 10: st.warning("⚠ī¸ Dataset too small for machine learning (need at least 10 rows)") else: # Run ML pipeline with error handling with st.spinner("🤖 Running machine learning pipeline..."): result, error = safe_dataframe_operation(run_ml_pipeline, data_to_use) if error: st.markdown(f'
{error}
', unsafe_allow_html=True) st.session_state.operation_status['ML'] = 'error' else: st.session_state.operation_status['ML'] = 'success' else: st.warning("⚠ī¸ Please upload a dataset first in the Upload section") except Exception as e: error_msg = StreamlitExceptionHandler.handle_exception(e, "machine learning") st.markdown(f'
{error_msg}
', unsafe_allow_html=True) # --------------------------------------- # INSIGHTS PAGE # --------------------------------------- elif current_page == "insights": try: if st.session_state.data is not None: df = st.session_state.data # Generate insights with error handling with st.spinner("💡 Generating business insights..."): result, error = safe_dataframe_operation(generate_business_insights, df) if error: st.markdown(f'
{error}
', unsafe_allow_html=True) st.session_state.operation_status['Insights'] = 'error' else: st.session_state.operation_status['Insights'] = 'success' else: st.warning("⚠ī¸ Please upload a dataset first in the Upload section") except Exception as e: error_msg = StreamlitExceptionHandler.handle_exception(e, "insights generation") st.markdown(f'
{error_msg}
', unsafe_allow_html=True) # --------------------------------------- # CHATBOT PAGE # --------------------------------------- elif current_page == "chatbot": try: if st.session_state.data is not None: df = st.session_state.data # Run chatbot with error handling with st.spinner("🤖 Initializing chatbot..."): result, error = safe_dataframe_operation(data_chatbot, df) if error: st.markdown(f'
{error}
', unsafe_allow_html=True) st.session_state.operation_status['Chatbot'] = 'error' else: st.session_state.operation_status['Chatbot'] = 'success' else: st.warning("⚠ī¸ Please upload a dataset first in the Upload section") except Exception as e: error_msg = StreamlitExceptionHandler.handle_exception(e, "chatbot") st.markdown(f'
{error_msg}
', unsafe_allow_html=True) # --------------------------------------- # DATA QUALITY PAGE # --------------------------------------- elif current_page == "quality": try: if st.session_state.data is not None: df = st.session_state.data # Run quality report with error handling with st.spinner("📋 Generating quality report..."): from data_quality import quality_report result, error = safe_dataframe_operation(quality_report, df) if error: st.markdown(f'
{error}
', unsafe_allow_html=True) st.session_state.operation_status['Data Quality'] = 'error' else: st.session_state.operation_status['Data Quality'] = 'success' else: st.warning("⚠ī¸ Please upload a dataset first in the Upload section") except Exception as e: error_msg = StreamlitExceptionHandler.handle_exception(e, "data quality") st.markdown(f'
{error_msg}
', unsafe_allow_html=True) # --------------------------------------- # STATISTICAL ANALYSIS PAGE # --------------------------------------- elif current_page == "statistical": try: if st.session_state.data is not None: df = st.session_state.data # Validate numeric data numeric_cols = df.select_dtypes(include=['number']).columns if len(numeric_cols) == 0: st.warning("⚠ī¸ No numeric columns found. Statistical analysis requires numeric data.") else: # Run statistical analysis with error handling with st.spinner("📐 Performing statistical analysis..."): from statistical_analysis import statistical_analysis result, error = safe_dataframe_operation(statistical_analysis, df) if error: st.markdown(f'
{error}
', unsafe_allow_html=True) st.session_state.operation_status['Statistical Analysis'] = 'error' else: st.session_state.operation_status['Statistical Analysis'] = 'success' else: st.warning("⚠ī¸ Please upload a dataset first in the Upload section") except Exception as e: error_msg = StreamlitExceptionHandler.handle_exception(e, "statistical analysis") st.markdown(f'
{error_msg}
', unsafe_allow_html=True) # --------------------------------------- # ERROR LOG DISPLAY (Hidden by default) # --------------------------------------- if st.session_state.error_log and st.checkbox("🔧 Show Error Log (Debug Mode)"): st.markdown("### 📋 Error Log") for i, error_entry in enumerate(st.session_state.error_log[-5:]): # Show last 5 errors with st.expander(f"Error {i+1}: {error_entry['timestamp'].strftime('%Y-%m-%d %H:%M:%S')}"): st.code(error_entry['error']) st.code(error_entry['traceback']) # --------------------------------------- # FOOTER # --------------------------------------- st.markdown("---") st.markdown( "

Made with ❤ī¸ using Streamlit | Version 2.0 | Enhanced Error Handling

", unsafe_allow_html=True )