from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from llama_cpp import Llama
from multiprocessing import Process, Queue
import uvicorn
from dotenv import load_dotenv
from difflib import SequenceMatcher

load_dotenv()

app = FastAPI()

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"},
]

llms = []
for model in models:
    llm = Llama.from_pretrained(repo_id=model['repo_id'], filename=model['filename'])
    llms.append(llm)

class ChatRequest(BaseModel):
    message: str
    top_k: int = 50
    top_p: float = 0.95
    temperature: float = 0.7

def generate_chat_response(request, queue):
    try:
        user_input = request.message
        responses = []
        for llm in llms:
            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']
            responses.append(reply)
        best_response = select_best_response(responses, request)
        queue.put(best_response)
    except Exception as e:
        queue.put(f"Error: {str(e)}")

def select_best_response(responses, request):
    coherent_responses = filter_by_coherence(responses, request)
    best_response = filter_by_similarity(coherent_responses)
    return best_response

def filter_by_coherence(responses, request):
    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):
    queue = Queue()
    p = Process(target=generate_chat_response, args=(request, queue))
    p.start()
    p.join()
    response = queue.get()
    if "Error" in response:
        raise HTTPException(status_code=500, detail=response)
    return {"response": response}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8001)