Spaces:
No application file
No application file
File size: 38,074 Bytes
da8e446 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 | 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("""
<style>
/* Global Styles */
.main {
padding: 0rem 1rem;
}
/* Header Styling */
.header-container {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
padding: 2rem;
border-radius: 20px;
margin-bottom: 2rem;
color: white;
text-align: center;
box-shadow: 0 10px 30px rgba(0,0,0,0.2);
}
.header-title {
font-size: 2.5rem;
font-weight: 700;
margin-bottom: 0.5rem;
animation: fadeInDown 1s;
}
.header-subtitle {
font-size: 1.1rem;
opacity: 0.95;
animation: fadeInUp 1s;
}
/* Card Styling */
.custom-card {
background: white;
padding: 1.5rem;
border-radius: 15px;
box-shadow: 0 5px 15px rgba(0,0,0,0.08);
margin-bottom: 1.5rem;
border: 1px solid rgba(0,0,0,0.05);
transition: transform 0.3s, box-shadow 0.3s;
}
.custom-card:hover {
transform: translateY(-5px);
box-shadow: 0 8px 25px rgba(0,0,0,0.15);
}
/* Error Message Styling */
.error-container {
background: linear-gradient(135deg, #ff6b6b 0%, #ff4757 100%);
color: white;
padding: 1.5rem;
border-radius: 15px;
margin: 1rem 0;
box-shadow: 0 10px 30px rgba(255, 71, 87, 0.3);
animation: slideInRight 0.5s;
}
.error-title {
font-size: 1.5rem;
font-weight: 700;
margin-bottom: 1rem;
}
.error-solution {
background: rgba(255, 255, 255, 0.2);
padding: 1rem;
border-radius: 10px;
margin-top: 1rem;
}
/* Success Message Styling */
.success-container {
background: linear-gradient(135deg, #51cf66 0%, #37b24d 100%);
color: white;
padding: 1rem;
border-radius: 10px;
margin: 1rem 0;
animation: fadeInUp 0.5s;
}
/* Warning Message Styling */
.warning-container {
background: linear-gradient(135deg, #ffd43b 0%, #fcc419 100%);
color: #2c3e50;
padding: 1rem;
border-radius: 10px;
margin: 1rem 0;
animation: fadeInUp 0.5s;
}
/* Metric Cards */
.metric-card {
background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
padding: 1.2rem;
border-radius: 12px;
text-align: center;
border-left: 4px solid #667eea;
}
.metric-value {
font-size: 2rem;
font-weight: 700;
color: #2c3e50;
margin: 0.5rem 0;
}
.metric-label {
font-size: 0.9rem;
color: #7f8c8d;
text-transform: uppercase;
letter-spacing: 1px;
}
/* Chatbot Styling */
.chat-container {
max-width: 800px;
margin: 2rem auto;
background: #f8f9fa;
border-radius: 20px;
padding: 1.5rem;
box-shadow: 0 5px 20px rgba(0,0,0,0.1);
}
.user-message {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 12px 18px;
border-radius: 20px 20px 5px 20px;
margin: 10px 0;
max-width: 80%;
margin-left: auto;
animation: slideInRight 0.5s;
}
.bot-message {
background: white;
color: #2c3e50;
padding: 12px 18px;
border-radius: 20px 20px 20px 5px;
margin: 10px 0;
max-width: 80%;
box-shadow: 0 2px 5px rgba(0,0,0,0.1);
animation: slideInLeft 0.5s;
}
/* Loading Spinner */
.custom-spinner {
border: 4px solid #f3f3f3;
border-top: 4px solid #667eea;
border-radius: 50%;
width: 40px;
height: 40px;
animation: spin 1s linear infinite;
margin: 20px auto;
}
@keyframes spin {
0% { transform: rotate(0deg); }
100% { transform: rotate(360deg); }
}
/* Animations */
@keyframes fadeInDown {
from {
opacity: 0;
transform: translateY(-20px);
}
to {
opacity: 1;
transform: translateY(0);
}
}
@keyframes fadeInUp {
from {
opacity: 0;
transform: translateY(20px);
}
to {
opacity: 1;
transform: translateY(0);
}
}
@keyframes slideInRight {
from {
opacity: 0;
transform: translateX(30px);
}
to {
opacity: 1;
transform: translateX(0);
}
}
@keyframes slideInLeft {
from {
opacity: 0;
transform: translateX(-30px);
}
to {
opacity: 1;
transform: translateX(0);
}
}
/* Responsive Design */
@media (max-width: 768px) {
.header-title {
font-size: 1.8rem;
}
.metric-value {
font-size: 1.5rem;
}
.user-message, .bot-message {
max-width: 95%;
}
}
/* Sidebar Styling */
.css-1d391kg {
background: linear-gradient(180deg, #f8f9fa 0%, #e9ecef 100%);
}
/* Button Styling */
.stButton > button {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
border: none;
padding: 0.5rem 2rem;
border-radius: 25px;
font-weight: 600;
transition: transform 0.2s, box-shadow 0.2s;
}
.stButton > button:hover {
transform: translateY(-2px);
box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
}
.stButton > button:disabled {
opacity: 0.5;
cursor: not-allowed;
}
/* Progress Bar */
.stProgress > div > div {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
}
/* Tabs */
.stTabs [data-baseweb="tab-list"] {
gap: 2rem;
background-color: #f8f9fa;
padding: 0.5rem;
border-radius: 30px;
}
.stTabs [data-baseweb="tab"] {
border-radius: 25px;
padding: 0.5rem 2rem;
}
/* Tooltip */
.tooltip {
position: relative;
display: inline-block;
cursor: help;
}
.tooltip .tooltiptext {
visibility: hidden;
width: 200px;
background-color: #555;
color: #fff;
text-align: center;
border-radius: 6px;
padding: 5px;
position: absolute;
z-index: 1;
bottom: 125%;
left: 50%;
margin-left: -100px;
opacity: 0;
transition: opacity 0.3s;
}
.tooltip:hover .tooltiptext {
visibility: visible;
opacity: 1;
}
</style>
""", unsafe_allow_html=True)
# ---------------------------------------
# HEADER WITH ANIMATION
# ---------------------------------------
st.markdown("""
<div class="header-container">
<div class="header-title">📊 AI Data Analyst Pro</div>
<div class="header-subtitle">Intelligent Data Analysis & Visualization Platform</div>
</div>
""", 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("""
<div class="warning-container">
<strong>⚠️ Data Quality Warnings:</strong><br>
""" + "<br>".join([f"• {issue}" for issue in issues]) + """
</div>
""", 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", "🛠️ Preprocessing", "🔍 EDA",
"📈 Visualization", "🤖 Machine Learning", "💡 Insights",
"💬 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"ℹ️ Data Quality: Good ({missing_pct:.1f}% missing)")
elif missing_pct < 20:
st.warning(f"⚠️ 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("📥 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"⏳ {op}")
else:
st.info("👆 Upload a dataset to get started")
# ---------------------------------------
# MAIN CONTENT AREA
# ---------------------------------------
# Map page names to functions
page_map = {
"📤 Upload Dataset": "upload",
"🛠️ Preprocessing": "preprocess",
"🔍 EDA": "eda",
"📈 Visualization": "visualization",
"🤖 Machine Learning": "ml",
"💡 Insights": "insights",
"💬 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('<div class="custom-card">', 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"⚠️ 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("""
<div class="success-container">
<strong>✅ Successfully loaded:</strong> {}<br>
<strong>📊 Shape:</strong> {} rows × {} columns
</div>
""".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("### 👁️ 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("🛠️ Go to Preprocessing", use_container_width=True):
st.session_state.page = "🛠️ 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'<div class="error-container">{error_msg}</div>', unsafe_allow_html=True)
# Log error
st.session_state.error_log.append({
'timestamp': datetime.now(),
'error': str(e),
'traceback': traceback.format_exc()
})
st.markdown('</div>', 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'<div class="error-container">{error}</div>', 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("""
<div class="success-container">
<strong>✅ Preprocessing completed successfully!</strong><br>
You can now proceed to analysis or visualization.
</div>
""", unsafe_allow_html=True)
else:
st.warning("⚠️ Please upload a dataset first in the Upload section")
except Exception as e:
error_msg = StreamlitExceptionHandler.handle_exception(e, "preprocessing")
st.markdown(f'<div class="error-container">{error_msg}</div>', 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'<div class="error-container">{error}</div>', unsafe_allow_html=True)
st.session_state.operation_status['EDA'] = 'error'
else:
st.session_state.operation_status['EDA'] = 'success'
else:
st.warning("⚠️ Please upload a dataset first in the Upload section")
except Exception as e:
error_msg = StreamlitExceptionHandler.handle_exception(e, "EDA")
st.markdown(f'<div class="error-container">{error_msg}</div>', 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'<div class="error-container">{error}</div>', unsafe_allow_html=True)
st.session_state.operation_status['Visualization'] = 'error'
else:
st.session_state.operation_status['Visualization'] = 'success'
else:
st.warning("⚠️ Please upload a dataset first in the Upload section")
except Exception as e:
error_msg = StreamlitExceptionHandler.handle_exception(e, "visualization")
st.markdown(f'<div class="error-container">{error_msg}</div>', 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("⚠️ 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'<div class="error-container">{error}</div>', unsafe_allow_html=True)
st.session_state.operation_status['ML'] = 'error'
else:
st.session_state.operation_status['ML'] = 'success'
else:
st.warning("⚠️ Please upload a dataset first in the Upload section")
except Exception as e:
error_msg = StreamlitExceptionHandler.handle_exception(e, "machine learning")
st.markdown(f'<div class="error-container">{error_msg}</div>', 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'<div class="error-container">{error}</div>', unsafe_allow_html=True)
st.session_state.operation_status['Insights'] = 'error'
else:
st.session_state.operation_status['Insights'] = 'success'
else:
st.warning("⚠️ Please upload a dataset first in the Upload section")
except Exception as e:
error_msg = StreamlitExceptionHandler.handle_exception(e, "insights generation")
st.markdown(f'<div class="error-container">{error_msg}</div>', 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'<div class="error-container">{error}</div>', unsafe_allow_html=True)
st.session_state.operation_status['Chatbot'] = 'error'
else:
st.session_state.operation_status['Chatbot'] = 'success'
else:
st.warning("⚠️ Please upload a dataset first in the Upload section")
except Exception as e:
error_msg = StreamlitExceptionHandler.handle_exception(e, "chatbot")
st.markdown(f'<div class="error-container">{error_msg}</div>', 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'<div class="error-container">{error}</div>', unsafe_allow_html=True)
st.session_state.operation_status['Data Quality'] = 'error'
else:
st.session_state.operation_status['Data Quality'] = 'success'
else:
st.warning("⚠️ Please upload a dataset first in the Upload section")
except Exception as e:
error_msg = StreamlitExceptionHandler.handle_exception(e, "data quality")
st.markdown(f'<div class="error-container">{error_msg}</div>', 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("⚠️ 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'<div class="error-container">{error}</div>', unsafe_allow_html=True)
st.session_state.operation_status['Statistical Analysis'] = 'error'
else:
st.session_state.operation_status['Statistical Analysis'] = 'success'
else:
st.warning("⚠️ Please upload a dataset first in the Upload section")
except Exception as e:
error_msg = StreamlitExceptionHandler.handle_exception(e, "statistical analysis")
st.markdown(f'<div class="error-container">{error_msg}</div>', 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(
"<p style='text-align: center; color: gray;'>Made with ❤️ using Streamlit | Version 2.0 | Enhanced Error Handling</p>",
unsafe_allow_html=True
) |