app.py

from pydantic import BaseModel
from llama_cpp_agent import Llama
from concurrent.futures import ThreadPoolExecutor, as_completed
import re
import httpx
import asyncio
import gradio as gr
import os
import gptcache
from dotenv import load_dotenv
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
import uvicorn
from threading import Thread

load_dotenv()

HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")

global_data = {
    'models': {},
    'tokens': {
        'eos': 'eos_token',
        'pad': 'pad_token',
        'padding': 'padding_token',
        'unk': 'unk_token',
        'bos': 'bos_token',
        'sep': 'sep_token',
        'cls': 'cls_token',
        'mask': 'mask_token'
    },
    'model_metadata': {},
    'max_tokens': 256,
    'tokenizers': {},
    'model_params': {},
    'model_size': {},
    'model_ftype': {},
    'n_ctx_train': {},
    'n_embd': {},
    'n_layer': {},
    'n_head': {},
    'n_head_kv': {},
    'n_rot': {},
    'n_swa': {},
    'n_embd_head_k': {},
    'n_embd_head_v': {},
    'n_gqa': {},
    'n_embd_k_gqa': {},
    'n_embd_v_gqa': {},
    'f_norm_eps': {},
    'f_norm_rms_eps': {},
    'f_clamp_kqv': {},
    'f_max_alibi_bias': {},
    'f_logit_scale': {},
    'n_ff': {},
    'n_expert': {},
    'n_expert_used': {},
    'causal_attn': {},
    'pooling_type': {},
    'rope_type': {},
    'rope_scaling': {},
    'freq_base_train': {},
    'freq_scale_train': {},
    'n_ctx_orig_yarn': {},
    'rope_finetuned': {},
    'ssm_d_conv': {},
    'ssm_d_inner': {},
    'ssm_d_state': {},
    'ssm_dt_rank': {},
    'ssm_dt_b_c_rms': {},
    'vocab_type': {},
    'model_type': {}
}

model_configs = [
    {"repo_id": "Hjgugugjhuhjggg/testing_semifinal-Q2_K-GGUF", "filename": "testing_semifinal-q2_k.gguf", "name": "testing"}
]

class ModelManager:
    def __init__(self):
        self.models = {}

    def load_model(self, model_config):
        if model_config['name'] not in self.models:
            try:
                self.models[model_config['name']] = Llama.from_pretrained(
                    repo_id=model_config['repo_id'],
                    filename=model_config['filename'],
                    use_auth_token=HUGGINGFACE_TOKEN,
                    n_threads=8,
                    use_gpu=False
                )
            except Exception as e:
                pass

    def load_all_models(self):
        with ThreadPoolExecutor() as executor:
            for config in model_configs:
                executor.submit(self.load_model, config)
        return self.models

model_manager = ModelManager()
global_data['models'] = model_manager.load_all_models()

class ChatRequest(BaseModel):
    message: str

def normalize_input(input_text):
    return input_text.strip()

def remove_duplicates(text):
    lines = text.split('\n')
    unique_lines = []
    seen_lines = set()
    for line in lines:
        if line not in seen_lines:
            unique_lines.append(line)
            seen_lines.add(line)
    return '\n'.join(unique_lines)

def cache_response(func):
    def wrapper(*args, **kwargs):
        cache_key = f"{args}-{kwargs}"
        if gptcache.get(cache_key):
            return gptcache.get(cache_key)
        response = func(*args, **kwargs)
        gptcache.set(cache_key, response)
        return response
    return wrapper

@cache_response
def generate_model_response(model, inputs):
    try:
        response = model(inputs)
        return remove_duplicates(response['choices'][0]['text'])
    except Exception as e:
        return ""

def remove_repetitive_responses(responses):
    unique_responses = {}
    for response in responses:
        if response['model'] not in unique_responses:
            unique_responses[response['model']] = response['response']
    return unique_responses

async def process_message(message):
    inputs = normalize_input(message)
    with ThreadPoolExecutor() as executor:
        futures = [
            executor.submit(generate_model_response, model, inputs)
            for model in global_data['models'].values()
        ]
        responses = [{'model': model_name, 'response': future.result()} for model_name, future in zip(global_data['models'].keys(), as_completed(futures))]
    unique_responses = remove_repetitive_responses(responses)
    formatted_response = ""
    for model, response in unique_responses.items():
        formatted_response += f"**{model}:**\n{response}\n\n"
    return formatted_response

app = FastAPI()

@app.post("/generate")
async def generate(request: ChatRequest):
    response = await process_message(request.message)
    return JSONResponse(content={"response": response})

def run_uvicorn():
    uvicorn.run(app, host="0.0.0.0", port=7860)

iface = gr.Interface(
    fn=process_message,
    inputs=gr.Textbox(lines=2, placeholder="Enter your message here..."),
    outputs=gr.Markdown(),
    title="Multi-Model LLM API (CPU Optimized)",
    description="Enter a message and get responses from multiple LLMs using CPU."
)

def run_gradio():
    iface.launch(server_port=7860, prevent_thread_lock=True)

if __name__ == "__main__":
    Thread(target=run_uvicorn).start()
    Thread(target=run_gradio).start()