app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Define the BLOOM model name
model_name = "CreitinGameplays/bloom-3b-conversational"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_text(user_prompt):
  """Generates text using the BLOOM model from Hugging Face Transformers and removes the user prompt."""
  # Construct the full prompt with system introduction, user prompt, and assistant role
  prompt = f"<|system|> You are a helpful AI assistant. </s> <|prompter|> {user_prompt} </s> <|assistant|>"

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

  # Generate text with the complete prompt and limit the maximum length to 256 tokens
  output = model.generate(
      input_ids=prompt_encoded,
      max_length=256,
      num_beams=1,
      num_return_sequences=1,  # Generate only 1 sequence
      do_sample=True,  # Enable sampling for creativity
      top_k=50,  # Sample from the top 50 most likely tokens at each step
      top_p=0.15,  # Filter out highly probable unlikely continuations
      temperature=0.1,  # Control the randomness of the generated text (1.0 for default)
      repetition_penalty=1.165
  )

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

  # Extract the assistant's response (assuming it starts with "<|assistant|>")
  assistant_response = generated_text.split("<|assistant|>")[-1]

  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 BLOOM-3b-conversational (Loaded with Hugging Face Transformers)",
)

# Launch the Gradio interface
interface.launch()