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from dotenv import load_dotenv
import io
import streamlit as st
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import PydanticOutputParser
from langchain_anthropic import ChatAnthropic
from langchain_openai import ChatOpenAI
from pydantic import ValidationError
from langchain_core.pydantic_v1 import BaseModel, Field
from resume_template import Resume
from json import JSONDecodeError
import PyPDF2
import json
import time
import os
# Set the LANGCHAIN_TRACING_V2 environment variable to 'true'
os.environ['LANGCHAIN_TRACING_V2'] = 'true'
# Set the LANGCHAIN_PROJECT environment variable to the desired project name
os.environ['LANGCHAIN_PROJECT'] = 'Resume_Project'
load_dotenv()
def pdf_to_string(file):
"""
Convert a PDF file to a string.
Parameters:
file (io.BytesIO): A file-like object representing the PDF file.
Returns:
str: The extracted text from the PDF.
"""
pdf_reader = PyPDF2.PdfReader(file)
num_pages = len(pdf_reader.pages)
text = ''
for i in range(num_pages):
page = pdf_reader.pages[i]
text += page.extract_text()
file.close()
return text
def extract_resume_fields(full_text, model):
"""
Analyze a resume text and extract structured information using a specified language model.
Parameters:
full_text (str): The text content of the resume.
model (str): The language model object to use for processing the text.
Returns:
dict: A dictionary containing structured information extracted from the resume.
"""
# The Resume object is imported from the local resume_template file
with open("prompts/resume_extraction.prompt", "r") as f:
template = f.read()
parser = PydanticOutputParser(pydantic_object=Resume)
prompt_template = PromptTemplate(
template=template,
input_variables=["resume"],
partial_variables={"response_template": parser.get_format_instructions()},
)
# Invoke the language model and process the resume
# formatted_input = prompt_template.format_prompt(resume=full_text)
llm = llm_dict.get(model, ChatOpenAI(temperature=0, model=model))
# print("llm", llm)
# output = llm.invoke(formatted_input.to_string())
chain = prompt_template | llm | parser
output = chain.invoke(full_text)
# print(output) # Print the output object for debugging
print(output)
return output
# try:
# parsed_output = parser.parse(output.content)
# json_output = parsed_output.json()
# print(json_output)
# return json_output
# except ValidationError as e:
# print(f"Validation error: {e}")
# print(output)
# return output.content
# except JSONDecodeError as e:
# print(f"JSONDecodeError error: {e}")
# print(output)
# return output.content
def display_extracted_fields(obj, section_title=None, indent=0):
if section_title:
st.subheader(section_title)
for field_name, field_value in obj:
if isinstance(field_value, BaseModel):
display_extracted_fields(field_value, field_name, indent + 1)
elif isinstance(field_value, list):
st.write(" " * indent + field_name + ":")
for item in field_value:
if isinstance(item, BaseModel):
display_extracted_fields(item, None, indent + 1)
else:
st.write(" " * (indent + 1) + "- " + str(item))
else:
st.write(" " * indent + field_name + ": " + str(field_value))
st.title("Resume Parser")
llm_dict = {
"gpt-3.5-turbo": ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo"),
"sonnet": ChatAnthropic(model_name="claude-3-sonnet-20240229"),
}
selected_model = st.selectbox("Select a model", list(llm_dict.keys()))
uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")
if uploaded_file is not None:
if st.button("Convert PDF to Text"):
start_time = time.time()
text = pdf_to_string(uploaded_file)
extracted_fields = extract_resume_fields(text, selected_model)
end_time = time.time()
elapsed_time = end_time - start_time
st.write(f"Extraction completed in {elapsed_time:.2f} seconds")
display_extracted_fields(extracted_fields, "Extracted Resume Fields")
# for key, value in extracted_fields.items():
# st.write(f"{key}: {value}")
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