import gradio as gr
import torch
import spaces
import bitsandbytes as bnb
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

# Define the model name
model_name = "CreitinGameplays/ConvAI-9b"

# Quantization configuration with bitsandbytes settings
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config, low_cpu_mem_usage=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#model.to(device)

@spaces.GPU(duration=120)
def generate_text(user_prompt):
  """Generates text using the ConvAI model from Hugging Face Transformers and removes the user prompt."""
  # Construct the full prompt with system introduction, user prompt, and assistant role

  system = "You are a helpful AI language model called ChatGPT, your goal is helping users with their questions."
  
  prompt = f"<|system|> {system} </s> <|user|> {user_prompt} </s>"

  # Encode the entire prompt into tokens
  prompt_encoded = tokenizer.encode(prompt, return_tensors="pt").to(device)

  # Generate text with the complete prompt and limit the maximum length to 256 tokens
  output = model.generate(
      input_ids=prompt_encoded,
      max_length=1550,
      num_beams=1,
      num_return_sequences=1,  
      do_sample=True,  
      top_k=50,  
      top_p=0.9,  
      temperature=0.2,  
      repetition_penalty=1.2
  )

  # Decode the generated token sequence back to text
  generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

  # Extract the assistant's response
  assistant_response = generated_text.split("<|user|>")[-1]
  assistant_response = assistant_response.replace(f"{user_prompt}", "").strip()
  assistant_response = assistant_response.replace(system, "").strip()
  assistant_response = assistant_response.replace("<|system|>", "").strip()
  assistant_response = assistant_response.replace("<|assistant|>", "").strip()
  
  return assistant_response

# Define the Gradio interface
interface = gr.Interface(
  fn=generate_text,
  inputs=[
      gr.Textbox(label="Text Prompt", value="What's an AI?"),
  ],
  outputs="text",
  description="Interact with ConvAI (Loaded with Hugging Face Transformers)",
)


# Launch the Gradio interface
interface.launch()