Create app.py

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
+from spaces import GPU
+import httpx
+
+# 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/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/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 = []
+    
+    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):
+        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 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
+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
+
+# 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}
+"""
+
+# Plantilla de respuesta de chat
+def render_chat_template(message, response):
+    return chat_template.format(message=message, response=response)
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)