#!/usr/bin/env python3
import sys
import os
import argparse
from dotenv import load_dotenv
from openai import OpenAI
from pdfminer.high_level import extract_text

# Load environment variables from .env file
load_dotenv()

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

def analyze_resume(text):
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{
            "role": "system",
            "content": "Provide a concise summary of the resume, highlighting key skills and potential areas for improvement, in a few sentences."
        },
        {"role": "user", "content": text}],
        max_tokens=200  # Add a max_tokens parameter to limit the output length
    )
    return response

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Analyze resume text using OpenAI.")
    parser.add_argument("-f", "--file", help="Path to the file containing the resume text.")
    args = parser.parse_args()

    if args.file:
        try:
            with open(args.file, "r", encoding="latin-1") as f:
                text_content = f.read()
        except FileNotFoundError:
            print(f"Error: File not found: {args.file}")
            sys.exit(1)
    elif len(sys.argv) > 1:
        text_content = sys.argv[1]
    else:
        parser.print_help()
        sys.exit(1)

    response = analyze_resume(text_content)
    summary = response.choices[0].message.content

    # Print usage information
    input_tokens = response.usage.prompt_tokens
    output_tokens = response.usage.completion_tokens
    total_tokens = response.usage.total_tokens

    print(f"Summary: {summary}")
    print(f"\n--- Usage Information ---")
    print(f"Input tokens: {input_tokens}")
    print(f"Output tokens: {output_tokens}")
    print(f"Total tokens: {total_tokens}")
    print(f"Cost: ${total_tokens * 0.000001:.6f}") # rough estimate

    print("\n--- Summary from OpenAI ---")
    print(f"Total tokens used: {total_tokens}")