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| import os | |
| from google.colab import userdata | |
| import litellm | |
| from crewai import Agent, Task, Crew, Process | |
| from crewai_tools import SerperDevTool | |
| import pdfplumber | |
| from docx import Document | |
| import gradio as gr | |
| # Set up API keys | |
| litellm.api_key = userdata.get("GOOGLE_API_KEY") | |
| os.environ['SERPER_API_KEY'] = userdata.get('SERPER_API_KEY') | |
| # Define the LLM | |
| llm = "gemini/gemini-1.5-flash-exp-0827" # Your LLM model | |
| # Initialize the tool for internet searching capabilities | |
| tool = SerperDevTool() | |
| # Create the CV Analysis Agent | |
| cv_analysis_agent = Agent( | |
| role="CV Analyzer", | |
| goal='Analyze the given CV and extract key skills and experiences and make improvements if needed for portfolio creation.', | |
| verbose=True, | |
| memory=True, | |
| backstory=( | |
| "As a CV Analyzer, you are skilled in identifying key information " | |
| "from resumes to aid in building effective portfolios." | |
| "You can add relevant skills and job responsibilities evaluating the whole cv." | |
| ), | |
| tools=[tool], | |
| llm=llm, | |
| allow_delegation=True | |
| ) | |
| # Create the Portfolio Generation Agent | |
| portfolio_generation_agent = Agent( | |
| role='Portfolio Generator', | |
| goal='Generate a beautiful static HTML/CSS/JS landing portfolio webpage based on CV analysis.', | |
| verbose=True, | |
| memory=True, | |
| backstory=( | |
| "As a Portfolio Generator, you craft engaging web pages with effective functionalities and color combinations " | |
| "to showcase individual talents and experiences with the best user experience." | |
| ), | |
| tools=[tool], | |
| llm=llm, | |
| allow_delegation=False | |
| ) | |
| # Research task for CV analysis | |
| cv_analysis_task = Task( | |
| description=( | |
| "Analyze the provided {cv} and identify key skills, experiences, " | |
| "and accomplishments. Highlight notable projects and educational background." | |
| ), | |
| expected_output='A summary of skills, experiences, and projects formatted for a portfolio.', | |
| tools=[tool], | |
| agent=cv_analysis_agent, | |
| ) | |
| # Writing task for portfolio generation with enhanced UI requirements | |
| portfolio_task = Task( | |
| description=( | |
| "Generate a static HTML/CSS/JS landing portfolio with a name as header in top, navbar for different sections, beautiful and responsive design. " | |
| "Ensure that the layout is clean, with sections for skills, projects, experiences, certifications, publications, and contact details if present in the CV. " | |
| "Include a footer that does not overlap with the content. " | |
| "Use a modern color palette and incorporate CSS frameworks if necessary, " | |
| "but provide everything embedded in the HTML file. " | |
| "The output should be a complete HTML document starting from <html> to </html>, ready to deploy." | |
| ), | |
| expected_output='A complete HTML/CSS/JS code content only for a portfolio website in a single .html file', | |
| tools=[tool], | |
| agent=portfolio_generation_agent, | |
| async_execution=False, | |
| output_file='portfolio.html' # Output as HTML file | |
| ) | |
| # Function to read CV from PDF or DOCX file | |
| def read_cv_file(file_path): | |
| ext = os.path.splitext(file_path)[1].lower() | |
| cv_content = "" | |
| if ext == '.pdf': | |
| with pdfplumber.open(file_path) as pdf: | |
| for page in pdf.pages: | |
| cv_content += page.extract_text() | |
| elif ext == '.docx': | |
| doc = Document(file_path) | |
| for para in doc.paragraphs: | |
| cv_content += para.text + "\n" | |
| else: | |
| raise ValueError("Unsupported file format. Please use .pdf or .docx.") | |
| return cv_content.strip() | |
| # Create a Crew for processing | |
| crew = Crew( | |
| agents=[cv_analysis_agent, portfolio_generation_agent], | |
| tasks=[cv_analysis_task, portfolio_task], | |
| process=Process.sequential, | |
| ) | |
| # Function to process CV and generate portfolio | |
| import re | |
| # Function to process CV and generate portfolio | |
| def process_cv(file): | |
| try: | |
| cv_file_content = read_cv_file(file.name) | |
| result = crew.kickoff(inputs={'cv': cv_file_content}) | |
| # Print the entire result object to explore its contents (for debugging) | |
| print(result) | |
| # Convert the result to string | |
| html_output = str(result) | |
| # Use replace to remove '''html''' and ''' from the output | |
| clean_html_output = html_output.replace("'''html'''", '').replace("'''", '').strip() | |
| return clean_html_output # Return the cleaned HTML | |
| except Exception as e: | |
| return f"Error: {e}" | |
| # Gradio UI using Blocks | |
| with gr.Blocks() as iface: | |
| gr.Markdown("# CV-2-HTML AI Enhanced Portfolio Website Generation") | |
| gr.Markdown("Upload your CV in PDF or DOCX format to analyze its content and generate a portfolio.") | |
| # File input for uploading CV | |
| cv_input = gr.File(label="Upload your CV (.pdf or .docx)") | |
| # Output textbox for generated HTML | |
| output_textbox = gr.Textbox(label="Generated HTML", lines=20, placeholder="Your generated HTML will appear here...", interactive=True) | |
| # Process button | |
| process_button = gr.Button("Generate Portfolio") | |
| # Define the button actions | |
| process_button.click(fn=process_cv, inputs=cv_input, outputs=output_textbox) | |
| # Launch the Gradio interface | |
| iface.launch() | |