app.py

import gradio as gr
from huggingface_hub import InferenceClient
import PyPDF2
import io

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def pdf_to_text(pdf_file):
    if pdf_file is None:
        return ""
    text = ""
    try:
        pdf_reader = PyPDF2.PdfReader(pdf_file.name)  # Use the file path instead of bytes
        for page in pdf_reader.pages:
            text += page.extract_text() + "\n"
    except Exception as e:
        text = f"Error reading PDF: {str(e)}"
    return text

def update_resume(pdf_file):
    return pdf_to_text(pdf_file)

def get_system_prompt(resume, job_description):
    return f"""
    You are an AI job interviewer. You have the candidate's resume and the job description:

    Resume:
    {resume}

    Job Description:
    {job_description}

    Your task is to conduct a job interview by asking relevant behavioral and technical questions based on the candidate's resume and the job requirements. Follow these guidelines:
    1. Ask one question at a time.
    2. Start with a question directly related to the candidate's experience or skills mentioned in their resume.
    3. In subsequent questions, alternate between resume-based questions and job description-based questions.
    4. Make your questions specific and varied. Do not repeat questions.
    5. After each candidate response, briefly acknowledge their answer before asking the next question.
    6. Do not provide feedback on their answers or make hiring decisions.
    7. Phrase your questions in a professional and courteous manner.

    Begin the interview with a question based on the candidate's resume.
    """

def generate_question(history, resume, job_description):
    messages = [
        {"role": "system", "content": get_system_prompt(resume, job_description)},
    ]
    
    for human, ai in history:
        messages.append({"role": "user", "content": human})
        messages.append({"role": "assistant", "content": ai})
    
    if not history:
        messages.append({"role": "user", "content": "Please start the interview with the first question based on my resume."})
    else:
        messages.append({"role": "user", "content": "Thank you for that response. Please ask the next interview question, considering my resume and the job requirements."})
    
    response = client.chat_completion(messages, max_tokens=150, temperature=0.7)
    return response.choices[0].message.content

def respond(message, history, resume, job_description):
    bot_message = generate_question(history, resume, job_description)
    history.append((message, bot_message))
    return history, ""  # Return updated history and empty the message box

with gr.Blocks() as demo:
    gr.Markdown("# AI Job Interview Simulator")
    gr.Markdown("Upload your resume and provide the job description to start a personalized interview.")
    
    with gr.Row():
        with gr.Column(scale=1):
            pdf_input = gr.File(label="Upload Resume (PDF only)", file_types=[".pdf"])
            resume_text = gr.Textbox(lines=10, label="Extracted Resume Text", interactive=True)
            job_description = gr.Textbox(lines=10, label="Job Description")
        
        with gr.Column(scale=2):
            chatbot = gr.Chatbot(label="Interview Session")
    
    message = gr.Textbox(label="Your response")
    
    pdf_input.upload(fn=update_resume, inputs=[pdf_input], outputs=[resume_text])
    
    submit = gr.Button("Submit Response")
    submit.click(
        fn=respond,
        inputs=[message, chatbot, resume_text, job_description],
        outputs=[chatbot, message]
    )

    gr.Markdown("## Instructions:")
    gr.Markdown("1. Upload your resume as a PDF file.")
    gr.Markdown("2. Review and edit the extracted text if necessary.")
    gr.Markdown("3. Paste the job description.")
    gr.Markdown("4. Click 'Submit Response' to start the interview or answer questions.")
    gr.Markdown("5. Respond to each question in the 'Your response' box.")

demo.launch()