app.py

from fastapi import FastAPI, HTTPException, Request
from pydantic import BaseModel
import uvicorn
import requests
import os
import io
import time
import asyncio
from typing import List, Dict, Any
from tqdm import tqdm
from llama_cpp import Llama  # Asegúrate de ajustar esto según la biblioteca que utilices

app = FastAPI()

# Configuración de los modelos
model_configs = [
    {"repo_id": "Ffftdtd5dtft/gpt2-xl-Q2_K-GGUF", "filename": "gpt2-xl-q2_k.gguf", "name": "GPT-2 XL"},
    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-instruct-q2_k.gguf", "name": "Meta Llama 3.1-8B Instruct"},
    {"repo_id": "Ffftdtd5dtft/gemma-2-9b-it-Q2_K-GGUF", "filename": "gemma-2-9b-it-q2_k.gguf", "name": "Gemma 2-9B IT"},
    {"repo_id": "Ffftdtd5dtft/gemma-2-27b-Q2_K-GGUF", "filename": "gemma-2-27b-q2_k.gguf", "name": "Gemma 2-27B"},
    {"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-Q2_K-GGUF", "filename": "phi-3-mini-128k-instruct-q2_k.gguf", "name": "Phi-3 Mini 128K Instruct"},
    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-q2_k.gguf", "name": "Meta Llama 3.1-8B"},
    {"repo_id": "Ffftdtd5dtft/Qwen2-7B-Instruct-Q2_K-GGUF", "filename": "qwen2-7b-instruct-q2_k.gguf", "name": "Qwen2 7B Instruct"},
    {"repo_id": "Ffftdtd5dtft/starcoder2-3b-Q2_K-GGUF", "filename": "starcoder2-3b-q2_k.gguf", "name": "Starcoder2 3B"},
    {"repo_id": "Ffftdtd5dtft/Qwen2-1.5B-Instruct-Q2_K-GGUF", "filename": "qwen2-1.5b-instruct-q2_k.gguf", "name": "Qwen2 1.5B Instruct"},
    {"repo_id": "Ffftdtd5dtft/starcoder2-15b-Q2_K-GGUF", "filename": "starcoder2-15b-q2_k.gguf", "name": "Starcoder2 15B"},
    {"repo_id": "Ffftdtd5dtft/gemma-2-2b-it-Q2_K-GGUF", "filename": "gemma-2-2b-it-q2_k.gguf", "name": "Gemma 2-2B IT"},
    {"repo_id": "Ffftdtd5dtft/sarvam-2b-v0.5-Q2_K-GGUF", "filename": "sarvam-2b-v0.5-q2_k.gguf", "name": "Sarvam 2B v0.5"},
    {"repo_id": "Ffftdtd5dtft/WizardLM-13B-Uncensored-Q2_K-GGUF", "filename": "wizardlm-13b-uncensored-q2_k.gguf", "name": "WizardLM 13B Uncensored"},
    {"repo_id": "Ffftdtd5dtft/Qwen2-Math-72B-Instruct-Q2_K-GGUF", "filename": "qwen2-math-72b-instruct-q2_k.gguf", "name": "Qwen2 Math 72B Instruct"},
    {"repo_id": "Ffftdtd5dtft/WizardLM-7B-Uncensored-Q2_K-GGUF", "filename": "wizardlm-7b-uncensored-q2_k.gguf", "name": "WizardLM 7B Uncensored"},
    {"repo_id": "Ffftdtd5dtft/Qwen2-Math-7B-Instruct-Q2_K-GGUF", "filename": "qwen2-math-7b-instruct-q2_k.gguf", "name": "Qwen2 Math 7B Instruct"}
]

class ModelManager:
    def __init__(self):
        self.models = {}
        self.model_parts = {}
        self.load_lock = asyncio.Lock()
        self.index_lock = asyncio.Lock()
        self.part_size = 1024 * 1024  # Tamaño de cada parte en bytes (1 MB)

    async def download_model_to_memory(self, model_config):
        url = f"https://huggingface.co/{model_config['repo_id']}/resolve/main/{model_config['filename']}"
        print(f"Descargando modelo desde {url}")
        try:
            start_time = time.time()
            response = requests.get(url)
            response.raise_for_status()
            end_time = time.time()
            download_duration = end_time - start_time
            print(f"Descarga completa para {model_config['name']} en {download_duration:.2f} segundos")
            return io.BytesIO(response.content)
        except requests.RequestException as e:
            raise HTTPException(status_code=500, detail=f"Error al descargar el modelo: {e}")

    async def save_model_to_temp_file(self, model_config):
        model_file = await self.download_model_to_memory(model_config)
        temp_filename = f"/tmp/{model_config['filename']}"
        print(f"Guardando el modelo en {temp_filename}")
        with open(temp_filename, 'wb') as f:
            f.write(model_file.getvalue())
        print(f"Modelo guardado en {temp_filename}")
        return temp_filename

    async def load_model(self, model_config):
        async with self.load_lock:
            try:
                temp_filename = await self.save_model_to_temp_file(model_config)
                start_time = time.time()
                print(f"Cargando modelo desde {temp_filename}")
                # Asegúrate de usar el método correcto para cargar el modelo
                llama = Llama.load(temp_filename)
                end_time = time.time()
                load_duration = end_time - start_time
                if load_duration > 0:
                    print(f"Modelo {model_config['name']} tardó {load_duration:.2f} segundos en cargar, dividiendo automáticamente")
                    await self.handle_large_model(temp_filename, model_config)
                else:
                    print(f"Modelo {model_config['name']} cargado correctamente en {load_duration:.2f} segundos")

                tokenizer = llama.tokenizer
                model_data = {
                    'model': llama,
                    'tokenizer': tokenizer,
                    'pad_token': tokenizer.pad_token,
                    'pad_token_id': tokenizer.pad_token_id,
                    'eos_token': tokenizer.eos_token,
                    'eos_token_id': tokenizer.eos_token_id,
                    'bos_token': tokenizer.bos_token,
                    'bos_token_id': tokenizer.bos_token_id,
                    'unk_token': tokenizer.unk_token,
                    'unk_token_id': tokenizer.unk_token_id
                }

                self.models[model_config['name']] = model_data
            except Exception as e:
                print(f"Error al cargar el modelo: {e}")

    async def handle_large_model(self, model_filename, model_config):
        total_size = os.path.getsize(model_filename)
        num_parts = (total_size + self.part_size - 1) // self.part_size

        print(f"Modelo {model_config['name']} dividido en {num_parts} partes")
        with open(model_filename, 'rb') as file:
            for i in tqdm(range(num_parts), desc=f"Indexando {model_config['name']}"):
                start = i * self.part_size
                end = min(start + self.part_size, total_size)
                file.seek(start)
                model_part = io.BytesIO(file.read(end - start))
                await self.index_model_part(model_part, i)

    async def index_model_part(self, model_part, part_index):
        async with self.index_lock:
            part_name = f"part_{part_index}"
            print(f"Indexando parte {part_index}")
            temp_filename = f"/tmp/{part_name}.gguf"
            with open(temp_filename, 'wb') as f:
                f.write(model_part.getvalue())
            print(f"Parte {part_index} indexada y guardada")

    async def generate_response(self, user_input):
        results = []
        for model_name, model_data in self.models.items():
            print(f"Generando respuesta con el modelo {model_name}")
            try:
                tokenizer = model_data['tokenizer']
                input_ids = tokenizer(user_input, return_tensors="pt").input_ids
                outputs = model_data['model'].generate(input_ids)
                generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

                # Dividir el texto generado en partes
                parts = []
                while len(generated_text) > 1000:
                    part = generated_text[:1000]
                    parts.append(part)
                    generated_text = generated_text[1000:]
                parts.append(generated_text)

                results.append({
                    'model_name': model_name,
                    'generated_text_parts': parts
                })
            except Exception as e:
                print(f"Error al generar respuesta con el modelo {model_name}: {e}")
                results.append({'model_name': model_name, 'error': str(e)})

        return results

@app.post("/generate/")
async def generate(request: Request):
    data = await request.json()
    user_input = data.get('input', '')
    if not user_input:
        raise HTTPException(status_code=400, detail="Se requiere una entrada de usuario.")

    try:
        model_manager = ModelManager()
        tasks = [model_manager.load_model(config) for config in model_configs]
        await asyncio.gather(*tasks)
        responses = await model_manager.generate_response(user_input)
        return {"responses": responses}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

def start_uvicorn():
    uvicorn.run(app, host="0.0.0.0", port=7860)

if __name__ == "__main__":
    asyncio.run(start_uvicorn())