- Backend Architecture: Python-based with FastAPI framework
- Frontend: Responsive single-page application using HTML5, CSS3, and vanilla JavaScript
- Integration Layer: Webhook support for Discord, Slack, and Telegram notifications
- Data Processing: Python core libraries with specialized modules for file handling
- Question Processing Engine: Pattern matching algorithm with specialized solvers
- 🖥 vicky_app.py: Web server, API endpoints, HTML rendering, and system management
- 🧠 vicky_server.py: Question analysis, pattern matching, and solution generation
The webhook system uses a buffering mechanism to batch notifications, preventing overload while maintaining detailed logs.
🔹 Supported Platforms:
✅ Discord: Rich message formatting with embedded content
✅ Telegram: Direct messages to specified chat IDs
✅ Slack: Interactive messages through incoming webhooks
The engine uses regex patterns for:
- 📌 Contextual Understanding: Detecting specific question types
- 🏷 Command Recognition: Identifying code commands and parameters
- 📑 Assignment Classification: Routing questions to GA1-GA5 solvers
- 📂 File Association: Linking relevant uploaded files
- 🔑 Generates Unique IDs: 8-character identifiers for each file
- 🏷 Stores Metadata: Tracks filenames, timestamps, and file types
- 🔗 Provides Contextual Access: Allows referencing files by ID
- 📌 Type-Specific Handling: Different processing for ZIP archives vs. README files
- 🔒 Client-side implementation for security and performance
- 🏷 Intelligent Format Detection for automatic prefix correction
- 🚀 Robust Error Handling for malformed Base64 data
- 📋 Clipboard Integration for direct image pasting
- 🔓 Bypass CORS Restrictions via third-party proxy
- 📜 Render Live Content in sandboxed iframe
- 📖 Provide Source Viewing for analysis
- ✂ Enable HTML Copying for modification
- 🔹 Multiple Response Formats: JSON and HTML output
- 🔹 File Upload Support: Process questions with attached files
- 🔹 Notification Integration: Webhook notifications for API calls
- 🔹 Security Features: IP logging, optional rate limiting
- 🛡 IP Logging: Tracks API and UI interactions
- 🔑 Admin Endpoints: Secure access to logs and analytics
- 🔒 Environment Variables: Sensitive values stored securely
- ⚠ XSS Protection: HTML escaping for user-generated content
- ✅ Input Validation: Thorough validation of user inputs
- 💾 Message History: Displays assistant conversation
- 📝 Code Formatting: Syntax highlighting
- 📂 File Uploads: Drag-and-drop and button-based
- 📋 Copy Functionality: One-click copying of code blocks
- 🏷 Tab-Based Navigation for easy category switching
- 🔄 Dynamic Content Loading based on selection
- 🎯 Active State Tracking for visual feedback
- 📜 File Listing: Shows all uploaded files with metadata
- ⚙ File Actions: Use or delete files
- 📤 Upload Interface: Simple form-based upload
- 🌍 RESTful API Pattern with:
- 📜 Form Data:
multipart/form-datafor file uploads - 📡 JSON Responses: Structured data
- 📄 HTML Responses: Rendered content
- 📜 Form Data:
- 📢 Status Updates for real-time monitoring
- ✅ Discord: Webhook notifications--whenever anybody hit api
- ✅ Slack: Incoming webhook events--whenever anybody hit api
- ✅ CORS Proxy: Third-party service for HTML viewing
- ✅ Local File System: Persistent storage for uploads
1️⃣ User Input: Question text + optional file upload
2️⃣ Pattern Analysis: Classification of intent
3️⃣ Solver Selection: Routing to correct solver function
4️⃣ Response Generation: Formatted answer creation
5️⃣ Data Enrichment: Code blocks and structured formatting
6️⃣ Client Delivery: Rendering in chat interface
- 📈 Usage Analytics: Feature usage statistics
- 🔍 IP Logs: System access records
- 📊 API Statistics: Endpoint usage data
- 📌 Tool Usage Tracking: Popularity insights
- 🚨 Error Monitoring: Centralized logging
The Vicky Data Science Assistant Platform is a multi-layered system integrating modern web technologies with an advanced Python backend. The modular architecture ensures scalability and easy maintenance, while the comprehensive API enables external integrations. Its emphasis on user experience and powerful data processing makes it a robust tool for data science education and assistance. 🚀
vicky_server.py is the core component of the Vicky Data Science Assistant Platform, responsible for question processing, pattern matching, and executing specialized solution functions for assignments GA1-GA5.
- 🧩 GA1 Solutions: Data processing and file handling
- 🛠 GA2 Solutions: API integration, Docker, FastAPI, and deployment
- 🔍 GA3 Solutions: Network requests, text processing, data manipulation
- 🌐 GA4 Solutions: Web scraping, data extraction, automation
- 📊 GA5 Solutions: Advanced data analysis and cleaning operations
Each function follows a consistent structure, with docstrings documenting parameters, return values, and purpose.
# Compress an image losslessly to be under 1,500 bytes
def ga2_second_solution(query=None):
import os
from PIL import Image
default_image_path = "E:\\data science tool\\GA2\\iit_madras.png"
image_path = file_manager.get_file(default_image_path, query, "image")
max_bytes = 1500
with Image.open(image_path) as img:
compressed_path = "compressed_output.png"
img.save(compressed_path, format="PNG", optimize=True, compress_level=9)
return compressed_path# Extract movie data from IMDb within a specified rating range
def ga4_second_solution(query=None):
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
options = Options()
options.add_argument("--headless")
driver = webdriver.Chrome(options=options)
driver.get("https://www.imdb.com/chart/top")
# Scrape and return movies within rating rangeThe vicky_server.py module powers the Vicky Data Science Assistant with pattern recognition and specialized solvers, providing an efficient and scalable approach to data science queries. 🚀