Demo Building a Multi Agent System

from dotenv import load_dotenv
load_dotenv()

import os
import asyncio
import re
from pathlib import Path

from agents import Agent, Runner, ModelSettings
from agents.tool import function_tool

import fitz  # PyMuPDF for reading PDFs
import openai

RESUME_DIR = Path("/Users/gavinridgeway/Documents/Anaconda/AiAgent/Resume")

@function_tool(name_override="scan_resumes_for_keywords")
def scan_resumes_for_keywords(keywords: list[str]) -> list[dict]:
    """
    Scan all PDF resumes in RESUME_DIR for keyword occurrences.

    Returns a list of dicts:
    [
      {
        "filename": "resume.pdf",
        "keyword": "Python",
        "match_snippet": "...context around the match...",
        "page": 2
      },
      ...
    ]
    """
    results: list[dict] = []

    for file in RESUME_DIR.glob("*.pdf"):
        try:
            doc = fitz.open(str(file))
        except Exception as e:
            # Skip files that can't be opened
            continue

        for page in doc:
            text = page.get_text() or ""
            for kw in keywords:
                idx = text.lower().find(kw.lower())
                if idx >= 0:
                    start = max(0, idx - 75)
                    snippet = text[start:start + 250].strip()
                    results.append({
                        "filename": file.name,
                        "keyword": kw,
                        "match_snippet": snippet,
                        "page": page.number + 1
                    })
        doc.close()

    return results

@function_tool(name_override="extract_keywords_from_job_description")
def extract_keywords_from_job_description(job_text: str) -> list[str]:
    """
    Use the LLM to extract 10-15 key skills/tools/responsibilities from the job_text.
    Returns a list of keywords.
    """
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {
                "role": "system",
                "content": "Extract 10–15 key skills, tools, and responsibilities from this job description."
            },
            {"role": "user", "content": job_text},
        ],
        temperature=0.3
    )

    response_text = response["choices"][0]["message"]["content"]
    lines = response_text.splitlines()
    # Strip bullets, numbering, and any leading/trailing whitespace
    keywords = [line.strip(" -•*0123456789.").strip() for line in lines if line.strip()]
    return keywords

@function_tool(name_override="transcribe_interview")
def transcribe_interview(file_path: str) -> str:
    """
    Transcribe an audio file (wav, mp3, etc.) using OpenAI's Whisper model.
    Returns the transcript text.
    """
    with open(file_path, "rb") as audio_file:
        transcript = openai.Audio.transcribe(model="whisper-1", file=audio_file)
    # The response includes a 'text' field
    return transcript.get("text", "").strip()

@function_tool(name_override="analyze_interview_relevance")
def analyze_interview_relevance(interview_text: str, job_description: str) -> str:
    """
    Using the LLM, evaluate how well the interview questions align with the job description.
    Return a detailed, human-readable assessment.
    """
    system_msg = (
        "You are an HR assistant. Evaluate how well the interview questions align with the job description. "
        "Be specific and helpful. Mention which areas were strong, which were missing, and provide actionable suggestions."
    )

    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": system_msg},
            {
                "role": "user",
                "content": f"Interview:\n\n{interview_text}\n\nJob Description:\n\n{job_description}"
            }
        ],
        temperature=0.4
    )

    return response["choices"][0]["message"]["content"].strip()

recruiter_agent = Agent(
    name="Ai Recruiter Assistant",
    instructions="""
You are helping a recruiter. Workflow:
1) Extract keywords from the job description.
2) Scan local resumes for keyword matches.
3) Transcribe the interview audio file.
4) Analyze how well the interview questions align with the job description.
Return a single consolidated report containing:
- Extracted keywords
- Resume keyword matches (filename, keyword, snippet, page)
- Interview transcript summary and alignment feedback
Be concise but thorough; include actionable suggestions where appropriate.
""",
    tools=[
        extract_keywords_from_job_description,
        scan_resumes_for_keywords,
        transcribe_interview,
        analyze_interview_relevance
    ],
    model="gpt-4",
    model_settings=ModelSettings(truncation="auto")
)

JOB_DESCRIPTION = """
We're hiring a full-stack engineer with experience in React, Python, REST APIs, and deployment on cloud platforms like AWS or GCP.
The role involves building scalable services, collaborating with product and design, and occasionally supporting data engineering tasks.
Experience with containerization, CI/CD, and monitoring is a plus.
"""

INTERVIEW_AUDIO_PATH = "/Users/gavinridgeway/Documents/Anaconda/AiAgent/Resume/audio_interview.MP3"

prompt = f"""
Please process this job description:
{JOB_DESCRIPTION}

Then scan local resumes for matches using scan_resumes_for_keywords. Finally, transcribe the audio file at:
{INTERVIEW_AUDIO_PATH}
and analyze whether the interview questions align with the job description.
"""

async def main():
    result = await Runner.run(recruiter_agent, input=prompt)
    # The Runner returns a structured result — print the final output from the agent.
    print(result.final_output)

if __name__ == "__main__":
    asyncio.run(main())