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app.py

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+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/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"},
+]
+
+# Clase para gestionar modelos
+class ModelManager:
+    def __init__(self):
+        self.models = []
+    
+    def load_model(self, model_config):
+        print(f"Cargando modelo: {model_config['name']}...")
+        return {"model": Llama.from_pretrained(repo_id=model_config['repo_id'], filename=model_config['filename']), "name": model_config['name']}
+    
+    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['name']}")
+                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, model_data):
+    try:
+        user_input = normalize_input(request.message)
+        llm = model_data['model']
+        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, "model_name": model_data['name']}
+    except Exception as e:
+        return {"response": f"Error: {str(e)}", "literal": user_input, "model_name": model_data['name']}
+
+def normalize_input(input_text):
+    return input_text.strip()
+
+def remove_duplicates(text):
+    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)
+    text = text.replace('[/INST]', '')
+    lines = text.split('\n')
+    unique_lines = list(dict.fromkeys(lines))
+    return '\n'.join(unique_lines).strip()
+
+def remove_repetitive_responses(responses):
+    seen = set()
+    unique_responses = []
+    for response in responses:
+        normalized_response = remove_duplicates(response['response'])
+        if normalized_response not in seen:
+            seen.add(normalized_response)
+            unique_responses.append(response)
+    return unique_responses
+
+def select_best_response(responses):
+    print("Filtrando respuestas...")
+    responses = remove_repetitive_responses(responses)
+    responses = [remove_duplicates(response['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):
+    print("Ordenando respuestas por coherencia...")
+    responses.sort(key=len, reverse=True)
+    return responses
+
+def filter_by_similarity(responses):
+    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(model_data, request):
+    print(f"Generando respuesta con el modelo: {model_data['name']}...")
+    response = generate_chat_response(request, model_data)
+    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, model_data, request) for model_data 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)
+            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)

requirements.txt

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+fastapi
+uvicorn
+llama-cpp-python
+python-dotenv
+tqdm