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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import time
import numpy as np
from torch.nn import functional as F
import os
from threading import Thread
token = os.environ["HF_TOKEN"]
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,token=token)
tok = AutoTokenizer.from_pretrained("google/gemma-2b-it",token=token)
# using CUDA for an optimal experience
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
start_message = ""
def user(message, history):
# Append the user's message to the conversation history
return "", history + [[message, ""]]
def chat(message, history):
chat = []
for item in history:
chat.append({"role": "user", "content": item[0]})
if item[1] is not None:
chat.append({"role": "assistant", "content": item[1]})
chat.append({"role": "user", "content": message})
messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
# Tokenize the messages string
model_inputs = tok([messages], return_tensors="pt").to(device)
streamer = TextIteratorStreamer(
tok, timeout=10., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=1024,
do_sample=True,
top_p=0.95,
top_k=1000,
temperature=0.75,
num_beams=1,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
# Initialize an empty string to store the generated text
partial_text = ""
for new_text in streamer:
# print(new_text)
partial_text += new_text
# Yield an empty string to cleanup the message textbox and the updated conversation history
yield partial_text
demo = gr.ChatInterface(fn=chat, examples=[["Write me a poem about Machine Learning."]], title="gemma 2b-it")
demo.launch()
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