Update app.py
Browse files
app.py
CHANGED
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from flask import Flask, request, jsonify, render_template_string
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from vllm import LLM, SamplingParams
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from langchain_community.cache import GPTCache
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app = Flask(__name__)
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modelos = {
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"facebook/opt-125m": LLM(model="facebook/opt-125m"),
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"llama-3.2-1B": LLM(model="Hjgugugjhuhjggg/llama-3.2-1B-spinquant-hf"),
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"gpt2": LLM(model="gpt2")
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}
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caches = {
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nombre: GPTCache(modelo, max_size=1000)
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for nombre, modelo in modelos.items()
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}
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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html_code_docs = """
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<!DOCTYPE html>
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<html>
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@@ -63,6 +71,7 @@ html_code_docs = """
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</html>
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"""
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html_code_chatbot = """
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<!DOCTYPE html>
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<html>
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from flask import Flask, request, jsonify, render_template_string
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from vllm import LLM, SamplingParams
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from langchain_community.cache import GPTCache
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import torch
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app = Flask(__name__)
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# Verificar si hay una GPU disponible, si no usar la CPU
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Inicializar los modelos con el dispositivo adecuado (GPU o CPU)
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modelos = {
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"facebook/opt-125m": LLM(model="facebook/opt-125m", device=device),
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"llama-3.2-1B": LLM(model="Hjgugugjhuhjggg/llama-3.2-1B-spinquant-hf", device=device),
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"gpt2": LLM(model="gpt2", device=device)
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}
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# Configuraci贸n de cach茅 para los modelos
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caches = {
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nombre: GPTCache(modelo, max_size=1000)
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for nombre, modelo in modelos.items()
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}
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# Par谩metros de muestreo para la generaci贸n de texto
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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# C贸digo HTML para la documentaci贸n de la API
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html_code_docs = """
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<!DOCTYPE html>
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<html>
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</html>
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"""
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# C贸digo HTML para la interfaz del chatbot
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html_code_chatbot = """
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<!DOCTYPE html>
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<html>
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