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import openai
import json
def initialize_openai(api_key):
openai.api_key = api_key
def prepare_training_data(training_data):
training_prompts = []
for index, row in training_data.iterrows():
job_description = row['role']
candidateA_resume = row['candidateAResume']
candidateB_resume = row['candidateBResume']
candidateA_transcript = row['candidateATranscript']
candidateB_transcript = row['candidateBTranscript']
winner_id = row['winnerId']
prompt = f"Job Description:\n{job_description}\n\nCandidate A Resume:\n{candidateA_resume}\n\nCandidate B Resume:\n{candidateB_resume}\n\nCandidate A Transcript:\n{candidateA_transcript}\n\nCandidate B Transcript:\n{candidateB_transcript}\n\nPreferred Candidate:"
completion = f"{winner_id}"
training_prompts.append({"prompt": prompt, "completion": completion})
with open("training_data.jsonl", "w") as f:
for item in training_prompts:
f.write(json.dumps(item) + "\n")
def upload_training_data(file_path):
with open(file_path, "rb") as f:
response = openai.files.create(
file=f,
purpose='fine-tune'
)
print("response-upload--->", response)
return response.id
def create_fine_tuning_job(file_id):
response = openai.fine_tuning.jobs.create(
training_file=file_id,
model="gpt-3.5-turbo",
)
print("response-create--->",response)
return response.fine_tuned_model
def fine_tune_model(training_data):
# Prepare training data
prepare_training_data(training_data)
# Upload training data
file_id = upload_training_data("training_data.jsonl")
# Create fine-tuning job
fine_tuned_model = create_fine_tuning_job(file_id)
return fine_tuned_model
def extract_keywords(resume, job_description, model):
prompt = f"Extract key skills and qualifications from the following resume based on the job description:\n\nJob Description:\n{job_description}\n\nResume:\n{resume}\n\nKey Skills and Qualifications:"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
response = openai.chat.completions.create(model=model, messages=messages, max_tokens=100)
return response.choices[0].message.content
def rate_skills(transcript, job_description, model):
prompt = f"Rate the skills of the candidate based on the following interview transcript and job description:\n\nJob Description:\n{job_description}\n\nInterview Transcript:\n{transcript}\n\nSkill Ratings:"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
response = openai.chat.completions.create(model=model, messages=messages, max_tokens=100)
return response.choices[0].message.content
def compare_candidates(candidateA, candidateB, job_description, model):
prompt = f"Based on the following details, return the candidate_id of the candidate which is the best fit for the role:\n\nJob Description:\n{job_description}\n\nCandidate A:\n{candidateA}\n\nCandidate B:\n{candidateB}\n\nPreferred Candidate:, ONLY RETURN THE CANDIDATE ID which would be of format '8ab47434-09a9-44e6-8c77-f9fd20c57765'"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt},
{"role": "system", "content": "<candidate_id>"}
]
response = openai.chat.completions.create(model=model, messages=messages, max_tokens=100)
return response.choices[0].message.content |