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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import os
# Define the repository ID and access token
repo_id = "Mikhil-jivus/Llama-32-3B-FineTuned"
access_token = os.getenv('HF_TOKEN')
# Load the tokenizer and model from the Hugging Face repository
tokenizer = AutoTokenizer.from_pretrained(repo_id, use_auth_token=access_token)
model = AutoModelForCausalLM.from_pretrained(repo_id, use_auth_token=access_token)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
# Tokenize the input messages
input_text = system_message + " ".join([f"{msg['role']}: {msg['content']}" for msg in messages])
input_ids = tokenizer.encode(input_text, return_tensors="pt")
# Generate a response
chat_history_ids = model.generate(
input_ids,
max_length=max_tokens,
temperature=temperature,
top_p=top_p,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
)
# Decode the response
response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch() |