Spaces:
Sleeping
Sleeping
File size: 3,371 Bytes
416760a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
from PyPDF2 import PdfReader
from agents.agents import get_agent_groq
import json
import re
def parse_resume(path):
loader = PdfReader(path)
text=''
print(len(loader.pages))
for i in range(len(loader.pages)):
text+= loader.pages[i].extract_text()
return text
def parse_resumes(resumes_list):
resumes_text=[]
for resume in resumes_list:
loader = PdfReader(resume)
text=''
#print(len(loader.pages))
for i in range(len(loader.pages)):
text+= loader.pages[i].extract_text()
resumes_text.append(text)
return resumes_text
def generate_analysis(resume_text, job_listing_text,job_title_text, must_have,prompt_template):
agent = get_agent_groq()
resp = agent.invoke(prompt_template.format(resume=resume_text, job_listing=job_listing_text,job_title_text=job_title_text,must_have=must_have))
text_res=extract(resp.content)
#text_res=extract(text_res)
#chain = prompt | agent
print(text_res)
#text = resp.content
return text_res
def generate_sel_analysis(resume_text, job_listing_text,job_title_text, must_have,prompt_template):
#chain = prompt | agent
agent = get_agent_groq()
response = agent.invoke(prompt_template.format(resume=resume_text, job_listing=job_listing_text,job_title_text=job_title_text,must_have=must_have))
print(response.content)
text_res=extract_sel(response.content)
print(text_res)
return text_res
def extract(content):
json_pattern = r'```\n(.*?)\n```'
json_string = re.search(json_pattern, content, re.DOTALL).group(1)
# Load the extracted JSON string into a dictionary
data = json.loads(json_string)
new={}
# Print the extracted variables and their values
for key, value in data.items():
print(f"{key}: {value}")
new[key]=value
return new
def extract_mist(json_string):
# Load the extracted JSON string into a dictionary
data = json.loads(json_string)
new={}
# Print the extracted variables and their values
for key, value in data.items():
print(f"{key}: {value}")
new[key]=value
return new
def extract_sel(content):
try:
# Split the content by identifying each candidate section using the candidate names (bolded)
candidates = re.split(r'\*\*(.*?)\*\*', content) # Split on the pattern of bolded names
# The split result will have alternating candidate names and JSON sections
candidate_json_list = []
for i in range(1, len(candidates), 2): # Iterate over candidate name and their JSON parts
candidate_name = candidates[i].strip() # Candidate name
json_string = candidates[i+1].strip() # JSON string part
# Load the JSON string into a dictionary
candidate_data = json.loads(json_string)
candidate_json_list.append(candidate_data)
return candidate_json_list
except json.JSONDecodeError as e:
print(f"Error decoding JSON: {e}")
return []
def generate_adv(job_listing_text,job_title_text, prompt_template):
# if model_selection=="Groq":
agent = get_agent_groq()
resp = agent.invoke(prompt_template.format(job_listing=job_listing_text,job_title_text=job_title_text))
text = resp.content
print(text)
return text |