app.py

from transformers import AutoTokenizer, MistralForCausalLM
import torch
import gradio as gr
import random
from textwrap import wrap
from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
from peft import PeftModel, PeftConfig
import torch
import gradio as gr

# 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(user_input, system_prompt="You are an expert medical analyst:"):
    """
    Generates text using a large language model, given a user input and a system prompt.
    Args:
        user_input: The user's input text to generate a response for.
        system_prompt: Optional system prompt.
    Returns:
        A string containing the generated text.
    """
    # Combine user input and system prompt
    formatted_input = f"<s>[INST]{system_prompt} {user_input}[/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 = "OpenLLM-France/Claire-Mistral-7B-0.1"

# Instantiate the Tokenizer
tokenizer = AutoTokenizer.from_pretrained("OpenLLM-France/Claire-Mistral-7B-0.1", trust_remote_code=True, padding_side="left")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = 'left'
model = AutoModelForCausalLM.from_pretrained("OpenLLM-France/Claire-Mistral-7B-0.1")

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

    def predict(self, user_input, system_prompt="You are an expert medical analyst:"):
        # Combine user input and system prompt
        formatted_input = f"<s>[INST]{system_prompt} {user_input}[/INST]"

        # Encode user input
        user_input_ids = tokenizer.encode(formatted_input, 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 = 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 Claire Chat🚀"
description = "You can use this Space to test out the current model (ClaireLLM) or duplicate this Space and use it for any other model on 🤗HuggingFace. Join me on Discord to build together."
examples = [["Oueche Normal, Claire, ça va ou quoi?", "bonjour je m'appele Claire et je suis une assistante francophone-first conçu par openLLM"]]

iface = gr.Interface(
    fn=bot.predict,
    title=title,
    description=description,
    examples=examples,
    inputs=["text", "text"],  # Take user input and system prompt separately
    outputs="text",
    theme="ParityError/Anime"
)

iface.launch()