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
+import uvicorn
+from dotenv import load_dotenv
+from difflib import SequenceMatcher
+from tqdm import tqdm
+
+load_dotenv()
+
+app = FastAPI()
+
+# Configuración de los modelos
+models = [
+    {"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"},
+]
+
+# Cargar modelos en RAM solo una vez
+llms = [Llama.from_pretrained(repo_id=model['repo_id'], filename=model['filename']) for model in models]
+print(f"Modelos cargados en RAM: {[model['repo_id'] for model in models]}")
+
+class ChatRequest(BaseModel):
+    message: str
+    top_k: int = 50
+    top_p: float = 0.95
+    temperature: float = 0.7
+
+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 select_best_response(responses):
+    # Deduplicar respuestas
+    unique_responses = list(set(responses))
+    # Filtrar respuestas coherentes
+    coherent_responses = filter_by_coherence(unique_responses)
+    # Seleccionar la mejor respuesta
+    best_response = filter_by_similarity(coherent_responses)
+    return best_response
+
+def filter_by_coherence(responses):
+    # Implementa aquí un filtro de coherencia si es necesario
+    return responses
+
+def filter_by_similarity(responses):
+    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
+
+@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}")
+
+    # Utilizar un ThreadPoolExecutor para procesar los modelos en paralelo
+    with ThreadPoolExecutor() as executor:
+        futures = [executor.submit(generate_chat_response, request, llm) for llm in llms]
+        responses = []
+
+        for future in tqdm(as_completed(futures), total=len(futures), desc="Generando respuestas"):
+            response = future.result()
+            responses.append(response)
+            print(f"Modelo procesado: {response['literal'][:30]}...")
+
+    # Extraer respuestas de los diccionarios
+    response_texts = [resp['response'] for resp in responses]
+    
+    # Verificar si hay errores en las respuestas
+    error_responses = [resp for resp in responses if "Error" in resp['response']]
+    if error_responses:
+        error_response = error_responses[0]
+        raise HTTPException(status_code=500, detail=error_response['response'])
+    
+    # Seleccionar la mejor respuesta
+    best_response = select_best_response(response_texts)
+    
+    print(f"Mejor respuesta seleccionada: {best_response}")
+
+    return {
+        "best_response": best_response,
+        "all_responses": response_texts
+    }
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)