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| import os | |
| from threading import Thread | |
| from typing import Iterator | |
| import gradio as gr # Importing Gradio for creating UI interfaces. | |
| import spaces # Import for using Hugging Face Spaces functionalities. | |
| import torch # PyTorch library for deep learning applications. | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer # Import necessary components from Hugging Face's Transformers. | |
| # Constants for maximum token lengths and defaults. | |
| MAX_MAX_NEW_TOKENS = 2048 | |
| DEFAULT_MAX_NEW_TOKENS = 1024 | |
| MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) | |
| # Initial description for the UI interface, showcasing the AI version and creator. | |
| DESCRIPTION = """\ | |
| # Masher AI v6 7B | |
| This Space demonstrates Masher AI v6 7B by Maheswar. | |
| """ | |
| # Check for GPU availability, append a warning to the description if running on CPU. | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU! This demo does not work on CPU.</p>" | |
| # If a GPU is available, load the model and tokenizer with specific configurations. | |
| if torch.cuda.is_available(): | |
| model_id = "mahiatlinux/MasherAI-v6-7B" | |
| model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", load_in_4bit=True) | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| tokenizer.use_default_system_prompt = False | |
| # Define a function decorated to use GPU and enable queue for processing the generation tasks. | |
| def generate( | |
| message: str, | |
| chat_history: list[tuple[str, str]], | |
| system_prompt: str, | |
| max_new_tokens: int = 1024, | |
| temperature: float = 0.1, | |
| top_p: float = 0.9, | |
| top_k: int = 50, | |
| repetition_penalty: float = 1.2, | |
| ) -> Iterator[str]: | |
| # Preparing conversation history for processing. | |
| conversation = [] | |
| # Adding system prompt. | |
| # conversation.append({"from": "human", "value": system_prompt}) | |
| # Extending the conversation history with user and assistant interactions. | |
| for user, assistant in chat_history: | |
| conversation.extend([{"from": "human", "value": user}, {"from": "gpt", "value": assistant}]) | |
| # Adding the latest message from the user to the conversation. | |
| conversation.append({"from": "human", "value": message}) | |
| # Tokenize and prepare the input, handle exceeding token lengths. | |
| input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt", add_generation_prompt=True) | |
| if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: | |
| input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] | |
| gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") | |
| input_ids = input_ids.to(model.device) | |
| # Setup for asynchronous text generation. | |
| streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) | |
| generate_kwargs = dict( | |
| {"input_ids": input_ids}, | |
| streamer=streamer, | |
| max_new_tokens=max_new_tokens, | |
| do_sample=True, | |
| top_p=top_p, | |
| top_k=top_k, | |
| temperature=temperature, | |
| num_beams=1, | |
| repetition_penalty=repetition_penalty, | |
| ) | |
| t = Thread(target=model.generate, kwargs=generate_kwargs) | |
| t.start() | |
| # Collect and yield generated outputs as they become available. | |
| outputs = [] | |
| for text in streamer: | |
| outputs.append(text) | |
| yield "".join(outputs) | |
| # Setup Gradio interface for chat, including additional controls for the generation parameters. | |
| chat_interface = gr.ChatInterface( | |
| fn=generate, | |
| fill_height=True, | |
| additional_inputs=[ | |
| gr.Textbox(label="System prompt", lines=6), | |
| gr.Slider( | |
| label="Max new tokens", | |
| minimum=1, | |
| maximum=MAX_MAX_NEW_TOKENS, | |
| step=1, | |
| value=DEFAULT_MAX_NEW_TOKENS, | |
| ), | |
| gr.Slider( | |
| label="Temperature", | |
| minimum=0.1, | |
| maximum=4.0, | |
| step=0.1, | |
| value=0.6, | |
| ), | |
| gr.Slider( | |
| label="Top-p (nucleus sampling)", | |
| minimum=0.05, | |
| maximum=1.0, | |
| step=0.05, | |
| value=0.9, | |
| ), | |
| gr.Slider( | |
| label="Top-k", | |
| minimum=1, | |
| maximum=1000, | |
| step=1, | |
| value=50, | |
| ), | |
| gr.Slider( | |
| label="Repetition penalty", | |
| minimum=1.0, | |
| maximum=2.0, | |
| step=0.05, | |
| value=1.2, | |
| ), | |
| ], | |
| stop_btn=None, | |
| examples=[ | |
| # Examples to assist users in starting conversations with the AI. | |
| ], | |
| ) | |
| chatbot=gr.Chatbot(height=450, label='Gradio ChatInterface') | |
| # Setup and launch the Gradio demo with Blocks API. | |
| with gr.Blocks(css="style.css", fill_height=True) as demo: | |
| gr.Markdown(DESCRIPTION) | |
| chat_interface.render() | |
| # Main entry point to start the web application if this script is run directly. | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() | |