appprinxdddd.py

from pydantic import BaseModel
from llama_cpp import Llama
from concurrent.futures import ThreadPoolExecutor, as_completed
import re
import asyncio
import gradio as gr
import os
import spaces
from dotenv import load_dotenv
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
import urllib3
import time
import random

urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

app = FastAPI()

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_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"},
    {"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"}
]

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)
            except Exception as e:
                print(f"Error loading model {model_config['name']}: {e}")
                pass  # Add pass to handle exceptions during model loading

    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):
    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 = []
    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)

@spaces.GPU(queue=False, idle_timeout=0, timeout=0) 
def generate_model_response(model, inputs):
    try:
        response = model(inputs)
        return remove_duplicates(response['choices'][0]['text'])
    except Exception as e:
        if "You have exceeded your GPU quota" in str(e):
            time.sleep(random.uniform(1, 3))
            try:
                response = model(inputs)
                return remove_duplicates(response['choices'][0]['text'])
            except Exception as e2:
                print(f"Error generating model response (after retry): {e2}")
                pass  # Add pass to handle exceptions during retry
                return ""
        else:
            print(f"Error generating model response: {e}")
            pass  # Add pass to handle other exceptions
            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.post("/generate_multimodel")
async def api_generate_multimodel(request: Request):
    while True:
        try: 
            data = await request.json()
            message = data["message"]
            formatted_response = await process_message(message)
            return JSONResponse({"response": formatted_response})
        except Exception as e:
            print(f"Error in API request handling: {e}")
            pass  # Add pass to handle exceptions in API request handling
        time.sleep(300)

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

if __name__ == "__main__":
    port = int(os.environ.get("PORT", 7860))
    iface.launch(server_port=port)