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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()