app.py

from transformers import AutoTokenizer, MistralForCausalLM
import torch
import gradio as gr
import random
from textwrap import wrap
import random

# Functions to Wrap the Prompt Correctly

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

def multimodal_prompt(input_text, system_prompt="", max_length=512):
    """
    Generates text using a large language model, given a prompt and a device.
    Args:
        input_text: The input text to generate a response for.
        system_prompt: Optional system prompt.
        max_length: Maximum length of the generated text.
    Returns:
        A string containing the generated text.
    """
    # Modify the input text to include the desired format
    formatted_input = f"""<s>[INST]{input_text}[/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
base_model_id = "mistralai/Mistral-7B-v0.1"
model_directory = "Tonic/mistralmed"

# Instantiate the Tokenizer
# tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True, padding_side="left")
tokenizer = AutoTokenizer.from_pretrained("Tonic/mistralmed", trust_remote_code=True, padding_side="left")
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)

# Load the PEFT model with the specified configuration
#peft_model = AutoModelForCausalLM.from_pretrained(base_model_id, config=model_config)

# Load the PEFT model
peft_config = PeftConfig.from_pretrained("Tonic/mistralmed", token="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")
peft_model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True)
peft_model = PeftModel.from_pretrained(peft_model, "Tonic/mistralmed", token="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")

class ChatBot:
    def __init__(self):
        self.history = []

    def predict(self, input_text):
        # Encode user input
        user_input_ids = tokenizer.encode(input_text, return_tensors="pt")

        # Concatenate the user input with chat history
        if len(self.history) > 0:
            chat_history_ids = torch.cat([self.history, user_input_ids], dim=-1)
        else:
            chat_history_ids = user_input_ids

        # Generate a response using the PEFT model
        response = peft_model.generate(input_ids=chat_history_ids, max_length=512, pad_token_id=tokenizer.eos_token_id)

        # Update chat history
        self.history = chat_history_ids

        # Decode and return the response
        response_text = tokenizer.decode(response[0], skip_special_tokens=True)
        return response_text

bot = ChatBot()

title = "👋🏻Welcome to Tonic's MistralMed Chat🚀"
description = "You can use this Space to test out the current model (MistralMed) or duplicate this Space and use it for any other model on 🤗HuggingFace. Join me on Discord to build together."
examples = [["What is the boiling point of nitrogen?"]]

iface = gr.Interface(
    fn=bot.predict,
    title=title,
    description=description,
    examples=examples,
    inputs="text",
    outputs="text",
    theme="ParityError/Anime"
)

iface.launch()