Update app.py

app.py

@@ -7,43 +7,54 @@ import uvicorn
 from dotenv import load_dotenv
 from difflib import SequenceMatcher
 import re
-from spaces import GPU
-import httpx
+import spaces
 
-# Cargar variables de entorno
 load_dotenv()
 
-# Inicializar aplicación FastAPI
 app = FastAPI()
 
-# Diccionario global para almacenar los modelos
 global_data = {
     'models': []
 }
 
-# Configuración de los modelos (incluyendo los nuevos)
 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"},
-    # Otros modelos omitidos por espacio
+    {"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/Meta-Llama-3.1-70B-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-q2_k.gguf", "name": "Meta Llama 3.1-70B"},
+    {"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf", "name": "Mistral Nemo Instruct 2407"},
+    {"repo_id": "Ffftdtd5dtft/Hermes-3-Llama-3.1-8B-IQ1_S-GGUF", "filename": "hermes-3-llama-3.1-8b-iq1_s-imat.gguf", "name": "Hermes 3 Llama 3.1-8B"},
+    {"repo_id": "Ffftdtd5dtft/Phi-3.5-mini-instruct-Q2_K-GGUF", "filename": "phi-3.5-mini-instruct-q2_k.gguf", "name": "Phi 3.5 Mini Instruct"},
     {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-70B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-instruct-q2_k.gguf", "name": "Meta Llama 3.1-70B Instruct"},
     {"repo_id": "Ffftdtd5dtft/codegemma-2b-IQ1_S-GGUF", "filename": "codegemma-2b-iq1_s-imat.gguf", "name": "Codegemma 2B"},
+    {"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-IQ2_XXS-GGUF", "filename": "phi-3-mini-128k-instruct-iq2_xxs-imat.gguf", "name": "Phi 3 Mini 128K Instruct XXS"},
+    {"repo_id": "Ffftdtd5dtft/TinyLlama-1.1B-Chat-v1.0-IQ1_S-GGUF", "filename": "tinyllama-1.1b-chat-v1.0-iq1_s-imat.gguf", "name": "TinyLlama 1.1B Chat"},
+    {"repo_id": "Ffftdtd5dtft/Mistral-NeMo-Minitron-8B-Base-IQ1_S-GGUF", "filename": "mistral-nemo-minitron-8b-base-iq1_s-imat.gguf", "name": "Mistral NeMo Minitron 8B Base"},
     {"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf", "name": "Mistral Nemo Instruct 2407"}
 ]
 
-# Clase para gestionar modelos
 class ModelManager:
     def __init__(self):
         self.models = []
+        self.loaded = False
     
     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']}
     
-    @GPU(duration=0)
     def load_all_models(self):
+        if self.loaded:
+            print("Modelos ya están cargados. No es necesario volver a cargarlos.")
+            return self.models
+        
         print("Iniciando carga de modelos...")
-        with ThreadPoolExecutor(max_workers=len(model_configs)) as executor:
+        with ThreadPoolExecutor() 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"):
@@ -53,21 +64,23 @@ class ModelManager:
                     print(f"Modelo cargado exitosamente: {model['name']}")
                 except Exception as e:
                     print(f"Error al cargar el modelo: {e}")
+        
+        self.models = models
+        self.loaded = True
         print("Todos los modelos han sido cargados.")
-        return models
+        return self.models
 
-# Instanciar ModelManager y cargar modelos una sola vez
 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
+@spaces.GPU(duration=0)
 def generate_chat_response(request, model_data):
     try:
         user_input = normalize_input(request.message)
@@ -104,40 +117,44 @@ def remove_repetitive_responses(responses):
             unique_responses.append(response)
     return unique_responses
 
-# Manejo de errores en la inicialización de modelos (traza mencionada en el error)
-def handle_initialization_error(allow_token):
-    try:
-        client = httpx.Client()
-        pid = 0  # Variable que simula el proceso actual
-        assert client.allow(allow_token=allow_token, pid=pid) == httpx.codes.OK
-    except AssertionError:
-        raise HTTPException(status_code=500, detail="Error en la inicialización del cliente Spaces")
-
-# Ruta para generar chat en múltiples modelos
-@app.post("/chat/")
-async def chat(request: ChatRequest):
-    try:
-        # Simulación del error `AssertionError` durante la inicialización
-        allow_token = "test_token"
-        handle_initialization_error(allow_token)
-
-        with ThreadPoolExecutor() as executor:
-            futures = [executor.submit(generate_chat_response, request, model) for model in global_data['models']]
-            responses = [future.result() for future in as_completed(futures)]
-        unique_responses = remove_repetitive_responses(responses)
-        return {"responses": unique_responses}
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error procesando la solicitud: {str(e)}")
-
-# Uso de template `chat_template.default`
-chat_template = """
-User: {message}
-Bot: {response}
-"""
+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(dict.fromkeys(responses))
+    sorted_responses = sorted(unique_responses, key=lambda r: len(r), reverse=True)
+    return sorted_responses[0]
+
+@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() as executor:
+        futures = [executor.submit(generate_chat_response, request, model_data) 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}")
+
+    if not responses:
+        raise HTTPException(status_code=500, detail="Error: No se generaron respuestas.")
+    
+    best_response = select_best_response(responses)
+    
+    print(f"Mejor respuesta seleccionada: {best_response}")
 
-# Plantilla de respuesta de chat
-def render_chat_template(message, response):
-    return chat_template.format(message=message, response=response)
+    return {
+        "best_response": best_response,
+        "all_responses": responses
+    }
 
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=8000)