app.py

import gradio as gr
import os
import io
import PyPDF2
#from langchain.llms import OpenAIChat
from langchain_openai import ChatOpenAI

from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain import PromptTemplate


# Updated imports for Gradio components
from gradio.components import File, Textbox

def extract_text_from_pdf_binary(pdf_binary):
    text = ""
    pdf_data = io.BytesIO(pdf_binary)
    reader = PyPDF2.PdfReader(pdf_data)
    num_pages = len(reader.pages)

    for page in range(num_pages):
        current_page = reader.pages[page]
        page_text = current_page.extract_text()
        if page_text:  # Check if page_text is not None or empty
            text += page_text
    return text

def format_resume_to_yaml(api_key, file_content):
    # Set the API key for OpenAI
    os.environ['OPENAI_API_KEY'] = api_key

    # Check if the file content is not empty
    if not file_content:
        raise ValueError("The uploaded file is empty.")

    # Extract text from the uploaded PDF binary
    resume_text = extract_text_from_pdf_binary(file_content)

    template = """Format the provided resume to this YAML template:
    ---
    name: ''
    phoneNumbers:
    - ''
    websites:
    - ''
    emails:
    - ''
    dateOfBirth: ''
    addresses:
    - street: ''
      city: ''
      state: ''
      zip: ''
      country: ''
    summary: ''
    education:
    - school: ''
      degree: ''
      fieldOfStudy: ''
      startDate: ''
      endDate: ''
    workExperience:
    - company: ''
      position: ''
      startDate: ''
      endDate: ''
    skills:
    - name: ''
    certifications:
    - name: ''
    {chat_history}
    {human_input}"""

    prompt = PromptTemplate(
        input_variables=["chat_history", "human_input"],
        template=template
    )

    memory = ConversationBufferMemory(memory_key="chat_history")

    llm_chain = LLMChain(
        llm=ChatOpenAI(model="gpt-3.5-turbo"),
        prompt=prompt,
        verbose=True,
        memory=memory,
    )

    res = llm_chain.predict(human_input=resume_text)
    return res

def main():
    input_api_key = Textbox(label="Enter your OpenAI API Key")
    input_pdf_file = File(label="Upload your PDF resume", type="binary")
    output_yaml = Textbox(label="Formatted Resume in YAML")

    iface = gr.Interface(
        fn=format_resume_to_yaml,
        inputs=[input_api_key, input_pdf_file],
        outputs=output_yaml,
        title="Resume to YAML Formatter",
        description="""This tool allows you to upload a PDF resume and formats it into a YAML template, 
        suitable for modern Applicant Tracking Systems. Please provide your OpenAI API key (you can obtain one 
        from [OpenAI](https://platform.openai.com/api-keys)) and upload your resume as a PDF. The backend is powered by Python and 
        uses the LangChain 🦜️🔗 library for processing. For more information and updates, visit my [GitHub](https://github.com/barghavanii/Resume-analytics-).""",
        allow_flagging="never",
    )

    iface.launch(debug=True, share=True)

if __name__ == "__main__":
    main()