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Rename app.py to apxp.py
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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/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/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/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf", "name": "Mistral Nemo Instruct 2407"},
{"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/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/codegemma-2b-IQ1_S-GGUF", "filename": "codegemma-2b-iq1_s-imat.gguf", "name": "Codegemma 2B"},
]
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(0)
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)