import gradio as gr
import requests
import os
import re

from dotenv import load_dotenv

load_dotenv()

def get_feedback(
    education: str,
    experiences: str,
    projects: str,
    skills: str,
    description: str
):
    headers = {
        'accept': 'application/json',
        'Content-Type': 'application/json'
    }

    resume = {
        "education": education,
        "experience": experiences,
        "project": projects,
        "skill": skills,
        "description": description
    }

    model_config = {
        "max_new_tokens": 512,
        "top_p": 0.9,
        "temperature": 0.4,
        "do_sample": True
    }

    payload = {
        "resume": resume,
        "generation": model_config
    }

    results = requests.post(
        os.getenv("ENDPOINT_URI"),
        headers=headers,
        json=payload
    ).json()
    
    outputs = results["review"]
    # return outputs
    
    strengths, weaknesses, improvements = re.split("strengths\n|weaknesses\n|improvements\n", outputs)[1:]

    return strengths.rstrip(), weaknesses.rstrip(), improvements.rstrip()

with gr.Blocks(theme=gr.themes.Base(font=[gr.themes.GoogleFont("Poppins")])) as demo:
    gr.Markdown(
        """
        <div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">
            ReviceGraph
        </div>
        <div style="text-align: center; font-size: 18px; margin-bottom: 20px;">
            A demo application that provides review on a resume against the job market using the G-Retriever framework, an LLM powered by a Knowledge Graph.
        </div>
        <div style="text-align: left; font-size: 14px; margin-bottom: 20px;">
            *Currently, ReviceGraph only supports the English language.
        </div>
        """
    )
    with gr.Row(equal_height=True):
        with gr.Column():
            gr.Markdown("### Input")
            education = gr.Textbox(
                label="Education",
                lines=2,
                placeholder="e.g. Bachelor Degree of Mathematics at Institut Teknologi Sepuluh Nopember",
                info="The candidate's formal education."
            )
            experience = gr.Textbox(
                label="Experiences",
                lines=2,
                placeholder="e.g. Data Scientist Intern at Bank Rakyat Indonesia",
                info="The candidate's work experience and job descriptions. Could be intenship experience or professional experience."
            )
            project = gr.Textbox(
                label="Projects",
                lines=2,
                placeholder="e.g. Chatbot for Customer Service using LLM",
                info="Projects completed by the candidate and their description. Could be college project or internship project"
            )
            skill = gr.Textbox(
                label="Skills",
                lines=2,
                placeholder="e.g. Python, Pytorch, Machine Learning, Deep Learning",
                info="Skills possessed by the candidate. Could be technical skill, concept skill, or soft skill."
            )
            description = gr.Textbox(
                label="Description",
                lines=2,
                placeholder="e.g. I want to pursue my career as machine learning engineer",
                info="Candidate career orientation"
            )

            submit = gr.Button("Get Feedback")
        with gr.Column():
            # feedback = gr.Textbox(label="Feedback", interactive=False, lines=18)
            gr.Markdown("### Output")
            strengths = gr.Textbox(
                label="Strengths",
                lines=7,
                interactive=False,
                info="Potential reasons for the candidate to be accepted."
            )
            weaknesses = gr.Textbox(
                label="Weaknesses",
                lines=7,
                interactive=False,
                info="Potential reasons for the candidate to be rejected."
            )
            improvements = gr.Textbox(
                label="Improvements",
                lines=7,
                interactive=False,
                info="Recommendation for improvement on candidate's skills, experiences, or projects based on their weaknesses."
            )

        # with gr.Column():
        #     max_new_tokens = gr.Slider(
        #         minimum=256,
        #         maximum=512,
        #         value=512,
        #         step=1.0,
        #         info="Maximum number of tokens or words",
        #         label="Maximum Output Length",
        #         interactive=True
        #     )
        #     top_p = gr.Slider(
        #         minimum=0,
        #         maximum=1,
        #         value=0.9,
        #         step=0.01,
        #         label="Top P",
        #         interactive=True
        #     )
        #     temperature = gr.Slider(
        #         minimum=0.01,
        #         maximum=1,
        #         value=0.4,
        #         step=0.01,
        #         info="Define how creative the model to generates feedback.\nThe optimal value is 0.4.",
        #         label="Temperature",
        #         interactive=True
        #     )

    submit.click(
        fn=get_feedback,
        inputs=[education, experience, project, skill, description], #, max_new_tokens, temperature, top_p],
        outputs=[strengths, weaknesses, improvements],
        # outputs=feedback,
        show_progress=True
    )

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