app.py

from fastapi import FastAPI, HTTPException, Request
from pydantic import BaseModel
from llama_cpp import Llama
from concurrent.futures import ThreadPoolExecutor, as_completed
import uvicorn
import re
from dotenv import load_dotenv
from spaces import GPU

load_dotenv()

app = FastAPI()

# Initialize ZeroGPU
try:
    GPU.initialize()
except Exception as e:
    print(f"ZeroGPU initialization failed: {e}")

# Global data dictionary
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.loaded = False

    def load_model(self, model_config):
        try:
            return {"model": Llama.from_pretrained(repo_id=model_config['repo_id'], filename=model_config['filename']), "name": model_config['name']}
        except Exception as e:
            print(f"Error loading model {model_config['name']}: {e}")
            return None

    def load_all_models(self):
        if self.loaded:
            return global_data['models']
        
        try:
            with ThreadPoolExecutor() as executor:
                futures = [executor.submit(self.load_model, config) for config in model_configs]
                models = []
                for future in as_completed(futures):
                    model = future.result()
                    if model:
                        models.append(model)
            
            global_data['models'] = {model['name']: model['model'] for model in models}
            self.loaded = True
            return global_data['models']
        except Exception as e:
            print(f"Error loading models: {e}")
            return {}

model_manager = ModelManager()
model_manager.load_all_models()

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

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:
            seen_lines.add(line)
            unique_lines.append(line)
    return '\n'.join(unique_lines)

def remove_repetitive_responses(responses):
    seen = set()
    unique_responses = []
    for response in responses:
        normalized_response = remove_duplicates(response['response'])
        if normalized_response not in seen:
            seen.add(normalized_response)
            unique_responses.append({'model': response['model'], 'response': normalized_response})
    return unique_responses

@app.post("/generate/")
@GPU(duration=0)
async def generate(request: ChatRequest):
    try:
        inputs = normalize_input(request.message)
        futures = [
            executor.submit(model.generate, inputs, top_k=request.top_k, top_p=request.top_p, temperature=request.temperature)
            for model in global_data['models'].values()
        ]
        responses = [{'model': model, 'response': future.result()} for model, future in zip(global_data['models'].keys(), as_completed(futures))]
        unique_responses = remove_repetitive_responses(responses)
        return unique_responses
    except Exception as e:
        print(f"Error generating responses: {e}")
        raise HTTPException(status_code=500, detail="Error generating responses")

@app.middleware("http")
async def process_request(request: Request, call_next):
    try:
        response = await call_next(request)
        return response
    except Exception as e:
        print(f"Request error: {e}")
        raise HTTPException(status_code=500, detail="Internal Server Error")

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)