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import json | |
import os | |
import time | |
from textwrap import dedent | |
import openai | |
import streamlit as st | |
def generate_prompt(job_description: str) -> str: | |
""" | |
Get the information extraction prompt for the given job description: | |
- Asking for JSON output | |
- Keys are: job_title, company, key_skills (as list), | |
job_description_summary and job_responsibilities_summary | |
- Asking to complete summarization in two steps (CoT) | |
""" | |
# using textwrap.dedent to unindent strings, e.g. ignore tabs | |
prompt = dedent("""\ | |
Given the job description separated by <>, extract useful information. | |
Format your response as JSON with the following structure: | |
{ | |
"job_title": Job title, | |
"company": Company, | |
"key_skills": ["list", "of", "key", "skills"], | |
"job_description_summary": Job description summary, | |
"job_responsibilities_summary": Job responsibilities summary | |
} | |
To effectively complete the summarization, follow these steps: | |
- First, summarize the whole job description and write it as value for "job_description" key | |
- Then, summarize the job description summary with a focus on day-to-day responsibilities | |
""") | |
prompt += f"<{job_description}>" | |
return prompt | |
def ask_chatgpt(input_text: str) -> str: | |
""" Call OpenAI's gpt-3.5-turbo model API with prompt by 'generate_prompt()' function """ | |
openai.api_key = os.getenv('OPENAI_API_KEY') | |
prompt = generate_prompt(input_text) | |
messages = [{"role": "user", "content": prompt}] | |
response = openai.ChatCompletion.create( | |
model="gpt-3.5-turbo", | |
messages=messages | |
) | |
return response.choices[0].message["content"] | |
def toy_ask_chatgpt(input_text: str) -> str: | |
""" Try to call 'ask_chatgpt()' function and returns the same result if OpenAI key is not valid """ | |
try: | |
return ask_chatgpt(input_text) | |
except openai.error.AuthenticationError: | |
time.sleep(2) # wait two seconds | |
return "OpenAI key is not valid. Input was:\n\n" + generate_prompt(input_text) | |
def main(): | |
""" Build Streamlit app """ | |
st.header("LLM-based application template with Streamlit") | |
st.subheader("Information extraction system from job descriptions") | |
with open('sample_job_description.txt', 'r') as f: | |
sample_job_description = f.read() | |
input_text = st.text_area('Enter job description', height=500, value=sample_job_description) | |
if st.button('Extract information', use_container_width=True): | |
with st.spinner('In progress...'): | |
# replace with 'ask_chatgpt' if you have configured your key | |
model_output = toy_ask_chatgpt(input_text) | |
with st.expander("Input prompt", expanded=False): | |
st.text(generate_prompt(input_text)) | |
st.subheader("Full model output") | |
try: | |
model_output = json.loads(model_output) | |
st.json(model_output) | |
except json.JSONDecodeError: | |
st.text(model_output) | |
if __name__ == "__main__": | |
main() |
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