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import torch
from transformers import pipeline
import sys
import sys
# Cargar el pipeline de generación de texto
pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha", torch_dtype=torch.bfloat16, device_map="auto")
# Definir el prompt para generar un JSON con eventos anidados
prompt = (
"Genera un JSON que describa una serie de eventos consecutivos en un formato similar al siguiente:\n\n"
"{\n"
" \"events\": {\n"
" \"event\": {\n"
" \"event_number\": 1,\n"
" \"name\": \"conflict_start\",\n"
" \"description\": \"Tensions escalate between Iran and Israel\",\n"
" \"probability\": 70,\n"
" \"duration_days\": 30,\n"
" \"subevents\": {\n"
" \"event\": {\n"
" \"event_number\": 2,\n"
" \"name\": \"diplomatic_failure\",\n"
" \"description\": \"Diplomatic negotiations fail\",\n"
" \"probability\": 60,\n"
" \"duration_days\": 15,\n"
" \"subevents\": {\n"
" \"event\": {\n"
" \"event_number\": 3,\n"
" \"name\": \"military_clash\",\n"
" \"description\": \"Initial military clash at the border\",\n"
" \"probability\": 50,\n"
" \"duration_days\": 10,\n"
" \"subevents\": {\n"
" \"event\": [\n"
" {\n"
" \"event_number\": 4,\n"
" \"name\": \"escalation\",\n"
" \"description\": \"Conflict escalates into full-scale war\",\n"
" \"probability\": 40,\n"
" \"duration_days\": 180,\n"
" \"subevents\": {\n"
" \"event\": [\n"
" {\n"
" \"event_number\": 5,\n"
" \"name\": \"regional_involvement\",\n"
" \"description\": \"Other Middle Eastern countries get involved\",\n"
" \"probability\": 30,\n"
" \"duration_days\": 365,\n"
" \"subevents\": {\n"
" \"event\": [\n"
" {\n"
" \"event_number\": 6,\n"
" \"name\": \"ceasefire\",\n"
" \"description\": \"International powers broker a ceasefire\",\n"
" \"probability\": 20,\n"
" \"duration_days\": 30\n"
" },\n"
" {\n"
" \"event_number\": 7,\n"
" \"name\": \"prolonged_conflict\",\n"
" \"description\": \"Conflict continues for over a year\",\n"
" \"probability\": 50,\n"
" \"duration_days\": 365\n"
" }\n"
" ]\n"
" }\n"
" },\n"
" {\n"
" \"event_number\": 8,\n"
" \"name\": \"international_intervention\",\n"
" \"description\": \"UN or other international organizations intervene\",\n"
" \"probability\": 25,\n"
" \"duration_days\": 60\n"
" }\n"
" ]\n"
" }\n"
" },\n"
" {\n"
" \"event_number\": 9,\n"
" \"name\": \"containment\",\n"
" \"description\": \"Conflict is contained and doesn't escalate\",\n"
" \"probability\": 30,\n"
" \"duration_days\": 90\n"
" }\n"
" ]\n"
" }\n"
" },\n"
" \"event\": {\n"
" \"event_number\": 10,\n"
" \"name\": \"sanctions\",\n"
" \"description\": \"Increased sanctions on Iran\",\n"
" \"probability\": 70,\n"
" \"duration_days\": 180,\n"
" \"subevents\": {\n"
" \"event\": [\n"
" {\n"
" \"event_number\": 11,\n"
" \"name\": \"iran_retaliates\",\n"
" \"description\": \"Iran retaliates with cyberattacks\",\n"
" \"probability\": 40,\n"
" \"duration_days\": 60\n"
" },\n"
" {\n"
" \"event_number\": 12,\n"
" \"name\": \"israel_response\",\n"
" \"description\": \"Israel responds with targeted airstrikes\",\n"
" \"probability\": 50,\n"
" \"duration_days\": 60\n"
" }\n"
" ]\n"
" }\n"
" }\n"
" }\n"
" },\n"
" \"event\": {\n"
" \"event_number\": 13,\n"
" \"name\": \"diplomatic_success\",\n"
" \"description\": \"Successful diplomatic negotiations\",\n"
" \"probability\": 40,\n"
" \"duration_days\": 30,\n"
" \"subevents\": {\n"
" \"event\": [\n"
" {\n"
" \"event_number\": 14,\n"
" \"name\": \"peace_agreement\",\n"
" \"description\": \"Iran and Israel sign a peace agreement\",\n"
" \"probability\": 20,\n"
" \"duration_days\": 60\n"
" },\n"
" {\n"
" \"event_number\": 15,\n"
" \"name\": \"temporary_truce\",\n"
" \"description\": \"A temporary truce is established\",\n"
" \"probability\": 30,\n"
" \"duration_days\": 30\n"
" }\n"
" ]\n"
" }\n"
" }\n"
" }\n"
" }\n"
" }\n"
"}\n\n"
"Ahora, genera un JSON similar con eventos anidados, pero cambia los detalles y números para hacer que sea con el input que viene a continuacion, respondiendo solo el JSON: "
)
def generate(event):
# Generar el texto usando el modelo
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": prompt,
},
{"role": "user", "content": event},
]
prompt_tuned = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt_tuned, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
# Imprimir la salida generada
return outputs |