app.py

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


title = "Welcome to Tonic's 🐋🐳Orca-2-13B!"
description = "You can use [🐋🐳microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) Or clone this space to use it locally or on huggingface! [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)."

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_name = "microsoft/Orca-2-13b"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False,)
model.to(device)

class OrcaChatBot:
    def __init__(self, model, tokenizer, system_message="You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."):
        self.model = model
        self.tokenizer = tokenizer
        self.system_message = system_message
        self.conversation_history = None

    def predict(self, user_message, temperature=0.4, max_new_tokens=70, top_p=0.99, repetition_penalty=1.9):
        # Prepare the prompt
        prompt = f"<|im_start|>system\n{self.system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant" if self.conversation_history is None else self.conversation_history + f"<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant"

        # Encode the prompt
        inputs = self.tokenizer(prompt, return_tensors='pt', add_special_tokens=False)
        input_ids = inputs["input_ids"].to(self.model.device)

        # Generate a response
        output_ids = self.model.generate(
            input_ids,
            max_length=input_ids.shape[1] + max_new_tokens,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=repetition_penalty,
            pad_token_id=self.tokenizer.eos_token_id
        )

        # Decode the generated response
        response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)

        # Update conversation history
        self.conversation_history = self.tokenizer.decode(output_ids[0], skip_special_tokens=False)

        return response

Orca_bot = OrcaChatBot(model, tokenizer)

def gradio_predict(user_message, character_intro, max_new_tokens, temperature, top_p, repetition_penalty):
    # Prepend the character introduction to the user message if provided
    full_message = f"{system_message}\n{user_message}" if system_message else user_message
    return Orca_bot.predict(full_message, temperature, max_new_tokens, top_p, repetition_penalty)

iface = gr.Interface(
    fn=gradio_predict,
    title=title,
    description=description,
    inputs=[
        gr.Textbox(label="Your Message", type="text", lines=3),
        gr.Textbox(label="Introduce a Character Here or Set a Scene (system prompt)", type="text", lines=2),
        gr.Slider(label="Max new tokens", value=1200, minimum=25, maximum=4096, step=1),
        gr.Slider(label="Temperature", value=0.7, minimum=0.05, maximum=1.0, step=0.05),
        gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.01, maximum=0.99, step=0.05),
        gr.Slider(label="Repetition penalty", value=1.9, minimum=1.0, maximum=2.0, step=0.05)
    ],
    outputs="text",
    theme="ParityError/Anime"
)

# Launch the Gradio interface
iface.launch()