import spaces from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM import torch import gradio as gr import random from textwrap import wrap def wrap_text(text, width=90): lines = text.split('\n') wrapped_lines = [textwrap.fill(line, width=width) for line in lines] wrapped_text = '\n'.join(wrapped_lines) return wrapped_text @spaces.GPU def multimodal_prompt(user_input, system_prompt="You are an expert medical analyst:"): # Combine user input and system prompt formatted_input = f" [INST] {example_instruction} [/INST] {example_answer} [INST] {system_prompt} [/INST]" # Encode the input text encodeds = tokenizer(formatted_input, return_tensors="pt", add_special_tokens=False) model_inputs = encodeds.to(device) # Generate a response using the model output = model.generate( **model_inputs, max_length=max_length, use_cache=True, early_stopping=True, bos_token_id=model.config.bos_token_id, eos_token_id=model.config.eos_token_id, pad_token_id=model.config.eos_token_id, temperature=0.1, do_sample=True ) # Decode the response response_text = tokenizer.decode(output[0], skip_special_tokens=True) return response_text # Define the device device = "cuda" if torch.cuda.is_available() else "cpu" # Use the base model's ID model_id = "SuperAGI/SAM" tokenizer = AutoTokenizer.from_pretrained(model_id) # tokenizer.pad_token = tokenizer.eos_token # tokenizer.padding_side = 'left' # Specify the configuration class for the model #model_config = AutoConfig.from_pretrained(base_model_id) model = AutoModelForCausalLM.from_pretrained(model_id , torch_dtype=torch.float16 , device_map= "auto" ) class ChatBot: def __init__(self): # Initialize the ChatBot class with an empty history self.history = [] def predict(self, user_input, system_prompt="You are an expert medical analyst:" , example_instruction="produce a json", example_answer = "please dont make small talk "): # Combine the user's input with the system prompt formatted_input = f" [INST] {example_instruction} [/INST] {example_answer} [INST] {system_prompt} [/INST]" # Encode the formatted input using the tokenizer user_input_ids = tokenizer.encode(formatted_input, return_tensors="pt") # Generate a response using the PEFT model response = model.generate(input_ids=user_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id) # Decode the generated response to text response_text = tokenizer.decode(response[0], skip_special_tokens=True) return response_text # Return the generated response bot = ChatBot() title = "🚀👋🏻Welcome to Tonic's🤖SuperAGI/SAM Chat🚀" description = "SAM is an Agentic-Native LLM that excels at complex reasoning. You can use this Space to test out the current model [Tonic/superagi-sam](https://huggingface.co/Tonic/superagi-sam) or duplicate this Space and use it locally or on 🤗HuggingFace. [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)." examples = [["[Question:] What is the proper treatment for buccal herpes?", "You are a medicine and public health expert, you will receive a question, answer the question, and provide a complete answer"]] def main(): with gr.Blocks() as demo: gr.Markdown(title) gr.Markdown(description) with gr.Row(): with gr.Column(): example_instruction = gr.Textbox(label="Example Instruction") example_answer = gr.Textbox(label="Example Answer") with gr.Column(): user_input = gr.Textbox(label="Your Question") system_prompt = gr.Textbox(label="System Prompt", value="You are an expert medical analyst:") submit_btn = gr.Button("Submit") output = gr.Textbox(label="Response") submit_btn.click( fn=bot.predict, inputs=[example_instruction, example_answer, user_input, system_prompt], outputs=output ) demo.launch() if __name__ == "__main__": main()