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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from inference import get_bot_response
from rag import get_context
from config import config
from huggingface_hub import InferenceClient
model_name = "mistralai/Mistral-7B-Instruct-v0.2"
client = InferenceClient(model_name)
print("tokenizer start loading")
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
print("tokenizer loaded")
print("model start loading")
model = AutoModelForCausalLM.from_pretrained(model_name,
device_map="auto",
trust_remote_code=False,
revision="main")
print("model loaded")
# model = AutoModelForCausalLM.from_pretrained(config["model_checkpoint"],
# device_map="auto",
# trust_remote_code=False,
# revision="main")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
request = message
context = get_context(request, config["top_k"])
response = get_bot_response(request, context, model, tokenizer)
return response
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() |