app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from llama_cpp import Llama
from concurrent.futures import ThreadPoolExecutor, as_completed
from tqdm import tqdm
import uvicorn
from dotenv import load_dotenv
from difflib import SequenceMatcher
import re

# Cargar variables de entorno
load_dotenv()

# Inicializar aplicación FastAPI
app = FastAPI()

# Diccionario global para almacenar los modelos
global_data = {
    'models': []
}

# Configuración de los modelos
model_configs = [
    {"repo_id": "Ffftdtd5dtft/gpt2-xl-Q2_K-GGUF", "filename": "gpt2-xl-q2_k.gguf"},
    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-instruct-q2_k.gguf"},
    {"repo_id": "Ffftdtd5dtft/gemma-2-9b-it-Q2_K-GGUF", "filename": "gemma-2-9b-it-q2_k.gguf"},
    {"repo_id": "Ffftdtd5dtft/gemma-2-27b-Q2_K-GGUF", "filename": "gemma-2-27b-q2_k.gguf"},
    {"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-Q2_K-GGUF", "filename": "phi-3-mini-128k-instruct-q2_k.gguf"},
    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-q2_k.gguf"},
    {"repo_id": "Ffftdtd5dtft/Qwen2-7B-Instruct-Q2_K-GGUF", "filename": "qwen2-7b-instruct-q2_k.gguf"},
    {"repo_id": "Ffftdtd5dtft/starcoder2-3b-Q2_K-GGUF", "filename": "starcoder2-3b-q2_k.gguf"},
    {"repo_id": "Ffftdtd5dtft/Qwen2-1.5B-Instruct-Q2_K-GGUF", "filename": "qwen2-1.5b-instruct-q2_k.gguf"}
]

# Clase para gestionar modelos
class ModelManager:
    def __init__(self):
        self.models = []
    
    def load_model(self, model_config):
        print(f"Cargando modelo {model_config['repo_id']}...")
        return Llama.from_pretrained(repo_id=model_config['repo_id'], filename=model_config['filename'])
    
    def load_all_models(self):
        print("Iniciando carga de modelos...")
        with ThreadPoolExecutor(max_workers=len(model_configs)) as executor:
            futures = [executor.submit(self.load_model, config) for config in model_configs]
            models = []
            for future in tqdm(as_completed(futures), total=len(model_configs), desc="Cargando modelos", unit="modelo"):
                try:
                    model = future.result()
                    models.append(model)
                    print(f"Modelo cargado exitosamente: {model_configs[len(models)-1]['repo_id']}")
                except Exception as e:
                    print(f"Error al cargar el modelo: {e}")
        print("Todos los modelos han sido cargados.")
        return models

# Instanciar ModelManager y cargar modelos
model_manager = ModelManager()
global_data['models'] = model_manager.load_all_models()

# Modelo global para la solicitud de chat
class ChatRequest(BaseModel):
    message: str
    top_k: int = 50
    top_p: float = 0.95
    temperature: float = 0.7

# Función para generar respuestas de chat
def generate_chat_response(request, llm):
    try:
        user_input = normalize_input(request.message)
        response = llm.create_chat_completion(
            messages=[{"role": "user", "content": user_input}],
            top_k=request.top_k,
            top_p=request.top_p,
            temperature=request.temperature
        )
        reply = response['choices'][0]['message']['content']
        return {"response": reply, "literal": user_input}
    except Exception as e:
        return {"response": f"Error: {str(e)}", "literal": user_input}

def normalize_input(input_text):
    return input_text.strip()

def remove_duplicates(text):
    # Eliminar patrones repetitivos específicos
    text = re.sub(r'(Hello there, how are you\? \[/INST\]){2,}', 'Hello there, how are you? [/INST]', text)
    text = re.sub(r'(How are you\? \[/INST\]){2,}', 'How are you? [/INST]', text)
    
    # Eliminar el marcador [/INST]
    text = text.replace('[/INST]', '')
    
    # Generaliza la eliminación de duplicados
    lines = text.split('\n')
    unique_lines = list(dict.fromkeys(lines))
    return '\n'.join(unique_lines).strip()

def remove_repetitive_responses(responses):
    # Filtra respuestas repetitivas
    seen = set()
    unique_responses = []
    for response in responses:
        normalized_response = remove_duplicates(response)
        if normalized_response not in seen:
            seen.add(normalized_response)
            unique_responses.append(normalized_response)
    return unique_responses

def select_best_response(responses):
    print("Filtrando respuestas...")
    responses = remove_repetitive_responses(responses)
    responses = [remove_duplicates(response) for response in responses]
    unique_responses = list(set(responses))
    coherent_responses = filter_by_coherence(unique_responses)
    best_response = filter_by_similarity(coherent_responses)
    return best_response

def filter_by_coherence(responses):
    # Ordenar respuestas por longitud y similaridad para coherencia básica
    print("Ordenando respuestas por coherencia...")
    responses.sort(key=len, reverse=True)
    return responses

def filter_by_similarity(responses):
    # Seleccionar la respuesta más coherente y única
    print("Filtrando respuestas por similitud...")
    responses.sort(key=len, reverse=True)
    best_response = responses[0]
    for i in range(1, len(responses)):
        ratio = SequenceMatcher(None, best_response, responses[i]).ratio()
        if ratio < 0.9:
            best_response = responses[i]
            break
    return best_response

def worker_function(llm, request):
    print(f"Generando respuesta con el modelo {llm}...")
    response = generate_chat_response(request, llm)
    return response

@app.post("/generate_chat")
async def generate_chat(request: ChatRequest):
    if not request.message.strip():
        raise HTTPException(status_code=400, detail="The message cannot be empty.")
    
    print(f"Procesando solicitud: {request.message}")

    responses = []
    num_models = len(global_data['models'])

    with ThreadPoolExecutor(max_workers=num_models) as executor:
        futures = [executor.submit(worker_function, llm, request) for llm in global_data['models']]
        for future in tqdm(as_completed(futures), total=num_models, desc="Generando respuestas", unit="modelo"):
            try:
                response = future.result()
                responses.append(response['response'])
            except Exception as exc:
                print(f"Error en la generación de respuesta: {exc}")

    best_response = select_best_response(responses)
    
    print(f"Mejor respuesta seleccionada: {best_response}")

    return {
        "best_response": best_response,
        "all_responses": responses
    }

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)