AI-Powered Workflow Automation Suite

AI-powered automation suite designed to streamline various tasks through advanced language model capabilities and specialized tools. The system integrates multiple functionalities to enhance productivity and content creation workflows.

System Overview and Architecture

The AI-Powered Workflow Automation Suite represents a sophisticated integration of artificial intelligence, natural language processing, and specialized tooling designed to automate complex knowledge work tasks. At its core, the system leverages OpenAI's language models (primarily GPT-4o-mini) as the cognitive engine, surrounded by purpose-built components that extend its capabilities into specialized domains.
The architecture follows a modular design pattern with distinct functional layers:

• Foundation Layer: Environment configuration, API authentication, and core dependencies
• Tool Implementation Layer: Specialized modules for specific workflow tasks
• Orchestration Layer: LangChain-based agent framework for tool selection and execution
• Interface Layer: Command-line interaction system for user engagement

Foundation Layer: Technical Infrastructure

The system begins with comprehensive environment setup, leveraging dotenv for secure credential management:

# Load environment variables from .env file
load_dotenv()

# Set API keys
openai.api_key = os.getenv('OPENAI_API_KEY')
NEWS_API_KEY = os.getenv('NEWS_API_KEY')
FIGMA_API_KEY = os.getenv('FIGMA_API_KEY')
EMAIL_ADDRESS = os.getenv('EMAIL_ADDRESS')
EMAIL_PASSWORD = os.getenv('EMAIL_PASSWORD')
os.environ["SERPAPI_API_KEY"] = ""

This configuration ensures sensitive credentials remain secure while enabling authenticated access to multiple external APIs. The system imports a diverse set of libraries spanning media processing (moviepy, pydub), natural language processing (sklearn), document handling (fpdf), communication protocols (smtplib), and AI frameworks (langchain, openai).

Tool Implementation Layer: Specialized Capabilities

Video Content Transformation: Multi-Stage Processing Pipeline

The video processing module implements a sophisticated multi-stage pipeline:

• Audio Extraction: Separates audio stream from video content
• Chunking Logic: Divides long audio into manageable segments (60-second chunks)
• Transcription Engine: Applies OpenAI's Whisper model to convert speech to text
• Content Synthesis: Processes raw transcriptions into coherent, structured notes

The implementation features error handling and resource cleanup to manage temporary files:

def transcribe_audio_chunk(audio_chunk, chunk_number):
    chunk_path = f"chunk{chunk_number}.wav"
    audio_chunk.export(chunk_path, format="wav")
    with open(chunk_path, 'rb') as audio_file:
        response = openai.Audio.transcribe("whisper-1", audio_file)
    os.remove(chunk_path)  # Clean up the chunk file after transcribing
    return response['text']

This approach solves the challenge of processing long-form video content that would otherwise exceed token limits or processing capabilities of the underlying AI models.

Intelligent News Aggregation: Information Retrieval and Synthesis

The news aggregation module implements a sophisticated information retrieval and synthesis workflow:

• Query Analysis: Uses CountVectorizer from sklearn to extract meaningful search terms
• Multi-Source Retrieval: Queries TheNewsAPI with extracted keywords
• Content Extraction: Employs the newspaper library to parse and extract full article text
• Cross-Document Synthesis: Combines multiple sources into a coherent response

This implementation addresses the challenge of gathering distributed information across multiple news sources:

def extract_keywords(query):
    vectorizer = CountVectorizer(stop_words='english')
    X = vectorizer.fit_transform([query])
    return vectorizer.get_feature_names_out()

def fetch_news(keywords, language='en', limit=3):
    base_url = 'https://api.thenewsapi.com/v1/news/all'
    query = ' '.join(keywords)  # Join keywords to form a search query
    params = {
        'api_token': NEWS_API_KEY,
        'language': language,
        'limit': limit,
        'search': query
    }
    # API request and response handling...

The system incorporates error handling for network failures and implements a modular approach that separates concerns between keyword extraction, article retrieval, content extraction, and synthesis.

Design-to-Code Conversion: Visual to Functional Translation

The Figma-to-HTML/CSS module implements a bridge between visual design and functional implementation:

• API Integration: Authenticates and retrieves design specifications from Figma's API
• Element Extraction: Parses complex design files to identify key components and attributes
• Code Generation: Transforms visual specifications into functional code
• Output Management: Organizes generated code into appropriate file structures

The implementation handles complex design hierarchies:

def extract_design_elements(design_data):
    elements = []
    for page in design_data['document']['children']:
        for frame in page['children']:
            element = {
                'name': frame['name'],
                'width': frame['absoluteBoundingBox']['width'],
                'height': frame['absoluteBoundingBox']['height'],
                'color': frame.get('backgroundColor', {}),
                'font': frame.get('style', {}).get('fontFamily'),
                'font_size': frame.get('style', {}).get('fontSize'),
            }
            elements.append(element)
    return elements

This capability reduces the time-consuming process of translating visual designs into functional code, addressing a significant bottleneck in web development workflows.

Code Generation: Natural Language to Executable Code

The code generation module translates natural language descriptions into functional Python code:

• Prompt Engineering: Formats user requests for optimal code generation
• Model Selection: Employs GPT-4o-mini for its code synthesis capabilities
• Output Management: Provides options for displaying or saving generated code

The implementation includes interactive elements for user decision-making:

# Optionally, save the generated code to a file
save_option = input("\nDo you want to save the generated code to a file? (y/n): ").lower()
if save_option == "y":
    file_name = input("Enter the file name (e.g., generated_code.py): ")
    with open(file_name, "w") as file:
        file.write(generated_code)
    print(f"Code saved to {file_name}")

This addresses the challenge of rapidly prototyping solutions without extensive manual coding.

Document and Communication Management

The system includes comprehensive document creation and communication capabilities:

• PDF Generation: Creates formatted PDF documents from text content
• Email Composition: Assembles multi-part MIME messages with attachments
• SMTP Integration: Connects with email servers for message delivery
• Resource Management: Handles cleanup of temporary files after use

def send_email_with_pdf(subject, body, recipients, text_for_pdf):
    pdf_filename = "attachment.pdf"
    create_pdf(text_for_pdf, pdf_filename)

    msg = MIMEMultipart()
    msg['From'] = EMAIL_ADDRESS
    msg['Subject'] = subject
    msg.attach(MIMEText(body, 'plain'))

    # Attachment handling and email sending...
    
    os.remove(pdf_filename)  # Clean up the PDF file
    return "Email sent successfully!"

This implementation addresses the needs for formal document creation and distribution in professional workflows.

Orchestration Layer: Agent-Based Workflow Management

The orchestration layer employs LangChain's agent framework to create an intelligent decision-making system:

• Tool Registration: Defines each capability as a structured tool with explicit schemas
• Agent Initialization: Creates a zero-shot agent capable of selecting appropriate tools
• Prompt Engineering: Defines the operating context and capabilities for the agent
• Execution Management: Handles the invocation flow and exception management

# Define the agent prompt with additional context
agent_prompt = """
You are an intelligent assistant capable of performing the following tasks:
1. Convert a video into detailed notes.
2. Aggregate and summarize news articles.
3. Convert Figma designs into HTML/CSS code.
4. Generate Python code by given prompts.
5. Send an email with a PDF attachment created from provided text.
4. Convert text to a PDF file.
5. Save content to a specified directory.
6.internet acesses

When a user gives you a task, determine which tool to use and ask them for any necessary input parameters. 
If the user doesn't provide enough information, ask follow-up questions to gather the necessary details.

Task: {task}

Parameters:
{parameters}

Thought Process:
{agent_scratchpad}
"""

The system implements Pydantic models for structured argument validation:

class VideoToNotesArgs(BaseModel):
    video_path: str

class NewsAggregatorArgs(BaseModel):
    question: str

This ensures type safety and input validation across the system.

Interface Layer: User Interaction

The interface layer provides a straightforward command-line interaction model:

• Input Collection: Gathers task descriptions from users
• Context Management: Maintains conversation history for contextual awareness
• Response Formatting: Presents agent outputs in a readable format
• Exception Handling: Manages and communicates errors appropriately

# Main loop to handle user input
while True:
    task = input("Enter the task (type 'exit' to quit): ").strip()
    if task.lower() == 'exit':
        print("Exiting the program.")
        break
    response = handle_user_query(task)
    print("Response:", response)

Advanced Features and Technical Considerations

The system incorporates several advanced features:

• Chat History Management: Maintains context across interactions
• Structured Tool Definitions: Uses Pydantic models for type-safe interfaces
• Multi-Modal Processing: Handles text, audio, and visual inputs
• Internet Access Integration: Incorporates web search capabilities for information retrieval

Technical considerations for deployment include:

• API Rate Limiting: The system makes multiple API calls that may be subject to rate limits
• Computational Requirements: Video processing demands significant computational resources
• Security Implications: Email credentials and API keys require secure handling
• Error Resilience: Each subsystem includes error handling, but comprehensive logging would
  enhance operational visibility

This AI-Powered Workflow Automation Suite demonstrates the potential for AI to transform knowledge work by automating complex tasks across domains. By combining specialized tools with an intelligent orchestration layer, the system enables non-technical users to leverage sophisticated AI capabilities through natural language instructions.