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