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from pydantic import BaseModel
from llama_cpp import Llama
import re
import os
import gradio as gr
from dotenv import load_dotenv
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
import spaces
import urllib3
import random
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
app = FastAPI()
load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
global_data = {
'model': None,
'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"},
{"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"},
{"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-Q2_K-GGUF", "filename": "phi-3-mini-128k-instruct-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-70B-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/Qwen2-7B-Instruct-Q2_K-GGUF", "filename": "qwen2-7b-instruct-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/starcoder2-3b-Q2_K-GGUF", "filename": "starcoder2-3b-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/Qwen2-1.5B-Instruct-Q2_K-GGUF", "filename": "qwen2-1.5b-instruct-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-70B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-instruct-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/codegemma-2b-IQ1_S-GGUF", "filename": "codegemma-2b-iq1_s-imat.gguf"},
{"repo_id": "Ffftdtd5dtft/Phi-3.5-mini-instruct-Q2_K-GGUF", "filename": "phi-3.5-mini-instruct-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/TinyLlama-1.1B-Chat-v1.0-IQ1_S-GGUF", "filename": "tinyllama-1.1b-chat-v1.0-iq1_s-imat.gguf"},
{"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Minitron-8B-Base-IQ1_S-GGUF", "filename": "mistral-nemo-minitron-8b-base-iq1_s-imat.gguf"},
{"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf"}
]
class ModelManager:
def __init__(self):
self.model = None
def load_models(self):
models = []
for config in model_configs:
try:
model = Llama.from_pretrained(repo_id=config['repo_id'], filename=config['filename'], use_auth_token=HUGGINGFACE_TOKEN)
models.append(model)
except Exception:
continue
self.model = models
model_manager = ModelManager()
model_manager.load_models()
global_data['model'] = model_manager.model
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()
async def generate_combined_response(inputs):
combined_response = ""
top_p = round(random.uniform(0.01, 1.00), 2)
top_k = random.randint(1, 100)
temperature = round(random.uniform(0.01, 2.00), 2)
for model in global_data['model']:
try:
response = model(inputs, top_p=top_p, top_k=top_k, temperature=temperature)
combined_response += remove_duplicates(response['choices'][0]['text']) + "\n"
except Exception:
continue
return combined_response
async def process_message(message):
inputs = normalize_input(message)
combined_response = await generate_combined_response(inputs)
formatted_response = ""
for line in combined_response.split("\n"):
formatted_response += f"{line}\n\n"
return formatted_response
@app.post("/generate_multimodel")
async def api_generate_multimodel(request: Request):
data = await request.json()
message = data["message"]
formatted_response = await process_message(message)
return JSONResponse({"response": formatted_response})
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 a unified model.",
)
if __name__ == "__main__":
port = int(os.environ.get("PORT", 7860))
iface.launch(server_port=port)
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