import spaces
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import gradio as gr

text_generator = None
def init():
    huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
    if not huggingface_token:
        pass
        print("no HUGGINGFACE_TOKEN if you need set secret ")
        #raise ValueError("HUGGINGFACE_TOKEN environment variable is not set")
    
    model_id = "google/gemma-2-9b-it"
    
    device = "auto" # torch.device("cuda" if torch.cuda.is_available() else "cpu")
    dtype = torch.bfloat16
    
    tokenizer = AutoTokenizer.from_pretrained(model_id, token=huggingface_token)
    
    print(model_id,device,dtype)
    histories = []
    #model = None
    
    model = AutoModelForCausalLM.from_pretrained(
            model_id, token=huggingface_token ,torch_dtype=dtype,device_map=device
        )
    text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer,torch_dtype=dtype,device_map=device) #pipeline has not to(device)
    
    if next(model.parameters()).is_cuda:
        print("The model is on a GPU")
    else:
        print("The model is on a CPU")
    
    if text_generator.device == 'cuda':
        print("The pipeline is using a GPU")
    else:
        print("The pipeline is using a CPU")
    
    print("initialized")

@spaces.GPU(duration=120)
def generate_text(messages):
#    model = AutoModelForCausalLM.from_pretrained(
#       model_id, token=huggingface_token ,torch_dtype=dtype,device_map=device
#  )

    #text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer,torch_dtype=dtype,device_map=device) #pipeline has not to(device)
    result = text_generator(messages, max_new_tokens=256, do_sample=True, temperature=0.7)

    generated_output = result[0]["generated_text"]
    if isinstance(generated_output, list):
        for message in reversed(generated_output):
            if message.get("role") == "assistant":
                content= message.get("content", "No content found.")
                return content
            
        return "No assistant response found."
    else:
        return "Unexpected output format."



def call_generate_text(message, history):
   # history.append({"role": "user", "content": message})
    print(message)
    print(history)
   
    messages = history+[{"role":"user","content":message}]
    try:
        text = generate_text(messages)
        return text
    except RuntimeError  as e:
        print(f"An unexpected error occurred: {e}")
       
    return ""

demo = gr.ChatInterface(call_generate_text,type="messages")

if __name__ == "__main__":
    init()
    demo.launch(share=True)