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import gradio as gr
from huggingface_hub import InferenceClient
#from unsloth import FastLanguageModel
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
#client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
#client = InferenceClient("halme/id2223_lora_model")
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})
#response = ""
""" for message in client.chat_completion(messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p):
token = message.choices[0].delta.content
response += token
yield response """
""" model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "halme/id2223_lora_model", # YOUR MODEL YOU USED FOR TRAINING
max_seq_length = max_tokens,
dtype = None,
load_in_4bit = True,
) """
model = AutoPeftModelForCausalLM.from_pretrained(
"halme/id2223_lora_model", # YOUR MODEL YOU USED FOR TRAINING
)
tokenizer = AutoTokenizer.from_pretrained("halme/id2223_lora_model")
#FastLanguageModel.for_inference(model) # Enable native 2x faster inference
"""messages = [
{"role": "user", "content": "Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,"},
] """
inputs = tokenizer.apply_chat_template(
messages,
tokenize = True,
add_generation_prompt = True, # Must add for generation
return_tensors = "pt",
)
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
yield model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,
use_cache = True, temperature = 1.5, min_p = 0.1)
"""
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()
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