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import gradio as gr
import os
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, pipeline
import torch
# Define the model repository
REPO_NAME = 'schuler/experimental-JP47D20'
# REPO_NAME = 'schuler/experimental-JP47D21-KPhi-3-micro-4k-instruct'
# How to cache?
def load_model(repo_name):
tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)
generator_conf = GenerationConfig.from_pretrained(repo_name)
model = AutoModelForCausalLM.from_pretrained(repo_name, trust_remote_code=True, torch_dtype=torch.bfloat16)
return tokenizer, generator_conf, model
tokenizer, generator_conf, model = load_model(REPO_NAME)
global_error = ''
try:
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
except Exception as e:
global_error = f"Failed to load model: {str(e)}"
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
result = 'none'
try:
# Build the conversation prompt
prompt = ''
messages = []
if (len(system_message)>0):
prompt = "<|assistant|>"+system_message+f"<|end|>\n"
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})
for message in messages:
role = "<|assistant|>" if message['role'] == 'assistant' else "<|user|>"
prompt += f"\n{role}\n{message['content']}\n<|end|>\n"
prompt += f"\n<|user|>\n{message}\n<|end|><|assistant|>\n"
"""
# Generate the response
response_output = generator(
prompt,
generation_config=generator_conf,
max_new_tokens=64,
do_sample=True,
top_p=0.25,
repetition_penalty=1.2
)
generated_text = response_output[0]['generated_text']
# st.session_state.last_response = generated_text
# Extract the assistant's response
result = generated_text[len(prompt):].strip()
"""
result = message+':'+prompt
except Exception as error:
result = str(error)
yield result
"""
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
"""
"""
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." + global_error, 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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