app.py

import sys
import os
import json
import gradio as gr
sys.path.append('src')
from procesador_de_cvs_con_llm import ProcesadorCV

use_dotenv = False
if use_dotenv:
    from dotenv import load_dotenv
    load_dotenv("../../../../../../../apis/.env")
    api_key = os.getenv("OPENAI_API_KEY")

else:
    api_key = os.getenv("OPENAI_API_KEY")

unmasked_chars = 8
masked_key = api_key[:unmasked_chars] + '*' * (len(api_key) - unmasked_chars*2) + api_key[-unmasked_chars:]
print(f"API key: {masked_key}")

def process_cv(job_text, cv_text, req_experience, req_experience_unit, positions_cap, dist_threshold_low, dist_threshold_high):
    if dist_threshold_low >= dist_threshold_high:
        return {"error": "dist_threshold_low must be lower than dist_threshold_high."}
    
    if not isinstance(cv_text, str) or not cv_text.strip():
        return {"error": "Please provide the CV or upload a file."}
    
    # Convertir la experiencia requerida a meses si se introduce en años
    if req_experience_unit == "years":
        req_experience = req_experience * 12

    try:
        procesador = ProcesadorCV(api_key, cv_text, job_text, ner_pre_prompt, 
                                  system_prompt, user_prompt, ner_schema, response_schema)
        dict_respuesta = procesador.procesar_cv_completo(
            req_experience=req_experience,
            positions_cap=positions_cap,
            dist_threshold_low=dist_threshold_low,
            dist_threshold_high=dist_threshold_high
        )
        return dict_respuesta
    except Exception as e:
        return {"error": f"Processing error: {str(e)}"}

# Parámetros de ejecución:
job_text = "Generative AI engineer"
cv_sample_path = 'cv_examples/reddgr_cv.txt' # Ruta al fichero de texto con un currículo de ejemplo
with open(cv_sample_path, 'r', encoding='utf-8') as file:
    cv_text = file.read()
# Prompts:
with open('prompts/ner_pre_prompt.txt', 'r', encoding='utf-8') as f:
    ner_pre_prompt = f.read()
with open('prompts/system_prompt.txt', 'r', encoding='utf-8') as f:
    system_prompt = f.read()
with open('prompts/user_prompt.txt', 'r', encoding='utf-8') as f:
    user_prompt = f.read()
# Esquemas JSON:
with open('json/ner_schema.json', 'r', encoding='utf-8') as f:
    ner_schema = json.load(f)
with open('json/response_schema.json', 'r', encoding='utf-8') as f:
    response_schema = json.load(f)

# Fichero de ejemplo para autocompletar (opción que aparece en la parte de abajo de la interfaz de usuario):
with open('cv_examples/reddgr_cv.txt', 'r', encoding='utf-8') as file:
    cv_example = file.read()

default_parameters = [4, "years", 10, 0.5, 0.7] # Parámetros por defecto para el reinicio de la interfaz y los ejemplos predefinidos 

# Código CSS para truncar el texto de ejemplo en la interfaz (bloque "Examples" en la parte de abajo):
css = """
        table tbody tr {
            height: 2.5em; /* Set a fixed height for the rows */
            overflow: hidden; /* Hide overflow content */
        }

        table tbody tr td {
            overflow: hidden; /* Ensure content within cells doesn't overflow */
            text-overflow: ellipsis; /* Add ellipsis for overflowing text */
            white-space: nowrap; /* Prevent text from wrapping */
            vertical-align: middle; /* Align text vertically within the fixed height */
        }
        """

# Interfaz Gradio:
with gr.Blocks(css=css) as interface:
    gr.Markdown("""
    Evaluate a CV against a job position using AI. Enter the job title in the 'Vacancy Title' field and paste the CV in plain text in the 'CV in Text Format' box. Specify the desired experience in months or years using the 'Required Experience'. 

    For more control, expand the 'Advanced options' section to adjust the number of positions analyzed and set distance thresholds for relevance evaluation.

    Once all fields are set, click the 'Process' button to analyze the CV. The results will be displayed in a structured JSON format below. You can also reset the inputs using the 'Clear' button.

    At the bottom of the interface, you can find predefined examples to quickly test the app with sample data.
    """)
    # Inputs
    job_text_input = gr.Textbox(label="Vacancy Title", lines=1, placeholder="Enter the vacancy title")
    gr.Markdown("Required Experience")
    with gr.Row():
        req_experience_input = gr.Number(label="Required Experience", value=default_parameters[0], precision=0, elem_id="req_exp", show_label=False)
        req_experience_unit = gr.Dropdown(label="Period", choices=["months", "years"], value=default_parameters[1], elem_id="req_exp_unit", show_label=False)
    cv_text_input = gr.Textbox(label="CV in Text Format", lines=5, max_lines=5, placeholder="Enter the CV text")
    
    # Opciones avanzadas ocultas en un objeto "Accordion"
    with gr.Accordion("Advanced options", open=False):
        positions_cap_input = gr.Number(label="Maximum number of positions to extract", value=default_parameters[2], precision=0)
        dist_threshold_low_slider = gr.Slider(
            label="Minimum embedding distance threshold (equivalent position)", 
            minimum=0, maximum=1, value=default_parameters[3], step=0.05
        )
        dist_threshold_high_slider = gr.Slider(
            label="Maximum embedding distance threshold (irrelevant position)", 
            minimum=0, maximum=1, value=default_parameters[4], step=0.05
        )
    
    submit_button = gr.Button("Process")
    clear_button = gr.Button("Clear")
    
    output_json = gr.JSON(label="Result")

    # Ejemplos:
    examples = gr.Examples(
        examples=[
            ["Supermarket cashier", "Deli worker since 2021. Previously worked 2 months as a waiter in a tapas bar."] + default_parameters,
            ["Generative AI Engineer", cv_example] + default_parameters
        ],
        inputs=[job_text_input, cv_text_input, req_experience_input, req_experience_unit, positions_cap_input, dist_threshold_low_slider, dist_threshold_high_slider]
    )

    # Botón "Procesar"
    submit_button.click(
        fn=process_cv,
        inputs=[
            job_text_input, 
            cv_text_input, 
            req_experience_input, 
            req_experience_unit,
            positions_cap_input, 
            dist_threshold_low_slider, 
            dist_threshold_high_slider
        ],
        outputs=output_json
    )

    # Botón "Limpiar"
    clear_button.click(
        fn=lambda: ("","",*default_parameters),
        inputs=[],
        outputs=[
            job_text_input, 
            cv_text_input, 
            req_experience_input, 
            req_experience_unit,
            positions_cap_input, 
            dist_threshold_low_slider, 
            dist_threshold_high_slider
        ]
    )

    # Footer
    gr.Markdown("""
        <footer>
        <p>You can view the complete code for this app and the explanatory notebooks on 
        <a href='https://github.com/reddgr/procesador-de-curriculos-cv' target='_blank'>GitHub</a></p>
        <p>© 2024 <a href='https://talkingtochatbots.com' target='_blank'>talkingtochatbots.com</a></p>
        </footer>
    """)

# Lanzar la aplicación:
if __name__ == "__main__":
    interface.launch()