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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 a帽os
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)}"}
# Par谩metros de ejecuci贸n:
job_text = "Generative AI engineer"
cv_sample_path = 'cv_examples/reddgr_cv.txt' # Ruta al fichero de texto con un curr铆culo 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 (opci贸n 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] # Par谩metros por defecto para el reinicio de la interfaz y los ejemplos predefinidos
# C贸digo 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. Enter the desired experience in months or years under **'Required Experience'**. \
Expand the **'Advanced options'** section to adjust the number of positions analyzed and set distance thresholds for the matching \
score based on embeddings distance evaluation.
Click the **'Process'** button to analyze the CV. The results will be displayed in a structured JSON format below. \
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]
)
# Bot贸n "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
)
# Bot贸n "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>漏 2024 <a href='https://talkingtochatbots.com' target='_blank'>talkingtochatbots.com</a></p>
</footer>
""")
# Lanzar la aplicaci贸n:
if __name__ == "__main__":
interface.launch() |