app.py

import os
import gradio as gr
from openai import OpenAI

# Initialize OpenAI client (expects OPENAI_API_KEY in env)
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

def make_system_message(system_message, role, jobad, education, workexp, skills, ask_sugg):
    msg = (
        f"{system_message}\n\n"
        f"Role, Industry and Type of Organization: {role}\n"
        f"Job Ad Responsibilities and Key Requirements: {jobad}\n"
        f"Education, Training and Certifications: {education}\n"
        f"Work Experience: {workexp}\n"
        f"Skills: {skills}\n"
    )
    if ask_sugg:
        msg += " The user is also asking for suggestions of skills related to this role."
    return msg

def stream_chat(
    message,
    history,                # list[list[str, str]] from gr.Chatbot
    system_message,
    role,
    jobad,
    education,
    workexp,
    skills,
    ask_sugg,
    max_tokens,
    temp,
    top_p,
):
    """
    Streaming generator that yields the progressively updated chat history.
    """
    # 1) Build system + conversation messages
    sys_msg = make_system_message(system_message, role, jobad, education, workexp, skills, ask_sugg)

    messages = [{"role": "system", "content": sys_msg}]
    for user_msg, assistant_msg in (history or []):
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})
    messages.append({"role": "user", "content": message})

    # 2) Start streaming back to the Chatbot
    running_reply = ""
    # Optimistically show the assistant "typing"
    running_history = (history or []) + [[message, ""]]
    yield running_history

    try:
        response = client.chat.completions.create(
            model="gpt-4o-mini",   # adjust if needed
            messages=messages,
            max_tokens=int(max_tokens),
            temperature=float(temp),
            top_p=float(top_p),
            stream=True,
        )
        for chunk in response:
            if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
                token = chunk.choices[0].delta.content
                running_reply += token
                running_history[-1][1] = running_reply
                # Yield the whole history each time so the UI updates
                yield running_history
    except Exception as e:
        running_history[-1][1] = f"❌ An error occurred: {str(e)}"
        yield running_history


with gr.Blocks(title="Resumize – Customize your CV!") as demo:
    gr.Markdown("""
    # Resumize – Customize your CV!
    This app customizes your resume to best suit a specific role, industry, employer and job ad.
    Powered by OpenAI GPT-4o and domain expertise.
    """)

    chatbot = gr.Chatbot(height=400)

    with gr.Column():
        instructions = gr.Textbox(
            value=(
                "You are a friendly Chatbot, a career coach and a talented copywriter. "
                "You are trying to help a user customize their resume according to a specific role, employer organization and job Ad "
                "- based on user input. Include tips if some items are missing."
            ),
            label="Instructions to Bot",
            lines=4,
        )
        role = gr.Textbox(label="Role, Industry and Employer", placeholder="Describe the role, industry and employer you are applying to.")
        jobad = gr.Textbox(label="Job Ad Responsibilities and Key Requirements", placeholder="Paste/describe the job ad responsibilities and key requirements", lines=4)
        education = gr.Textbox(label="Your Education, Certifications, Training, etc.", placeholder="Degrees, diplomas, certifications, courses", lines=3)
        workexp = gr.Textbox(label="Your Work Experience", placeholder="Roles, responsibilities, achievements", lines=4)
        skills = gr.Textbox(label="Skills", placeholder="List your key skills that match this job or ask for suggestions")
        ask_sugg = gr.Checkbox(label="Ask for Skills Suggestions", value=False)

        with gr.Row():
            max_tokens = gr.Slider(minimum=1, maximum=4096, value=512, step=1, label="Max new tokens")
            temp = gr.Slider(minimum=0.0, maximum=2.0, value=0.7, step=0.1, label="Temperature")
            top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus)")

    msg = gr.Textbox(label="Type your message here...", placeholder="e.g., Draft a tailored resume summary")
    with gr.Row():
        send = gr.Button("Send", variant="primary")
        clear = gr.Button("Clear")

    # Wire up sending (submit or button) → stream_chat → chatbot
    inputs = [msg, chatbot, instructions, role, jobad, education, workexp, skills, ask_sugg, max_tokens, temp, top_p]
    outputs = [chatbot]

    msg.submit(stream_chat, inputs, outputs)
    send.click(stream_chat, inputs, outputs)

    # Clear everything
    def do_clear():
        return [], "", "", "", "", "", "", False, 512, 0.7, 0.95

    clear.click(
        do_clear,
        inputs=[],
        outputs=[chatbot, msg, instructions, role, jobad, education, workexp, skills, ask_sugg, max_tokens, temp, top_p],
    )

if __name__ == "__main__":
    demo.launch()