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import gradio as gr | |
from huggingface_hub import InferenceClient | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | |
import os | |
from threading import Thread | |
from datasets import load_dataset | |
ds = load_dataset("hichri-mo/twensa-hositng") | |
context=ds["text"] | |
system_prompt = f"""you are twensa hosting chat bot | |
to know more about twensa hosting this is an ad about them : | |
{context} | |
""" | |
model = AutoModelForCausalLM.from_pretrained("KingNish/Qwen2.5-0.5b-Test-ft", | |
torch_dtype=torch.float16) | |
tokenizer = AutoTokenizer.from_pretrained("KingNish/Qwen2.5-0.5b-Test-ft") | |
device = torch.device('cuda') | |
model = model.to(device) | |
def chat(message, history): | |
chat = [{"role":"system","content":system_prompt}] | |
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 = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) | |
# Tokenize the messages string | |
model_inputs = tokenizer([messages], return_tensors="pt").to(device) | |
streamer = TextIteratorStreamer( | |
tokenizer, 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: | |
partial_text += new_text | |
yield partial_text | |
demo = gr.ChatInterface(fn=chat, | |
chatbot=gr.Chatbot(show_label=True, show_share_button=True, show_copy_button=True,layout="bubble", bubble_full_width=False), | |
theme="dark", | |
examples=[["what is twensa hosting ?"]], | |
title="TWENSA HOSTING CHAT BOT") | |
# Launch the app | |
demo.launch() |