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import gradio as gr
import torch
import transformers
# from transformers import AutoTokenizer
from langchain import LLMChain, HuggingFacePipeline, PromptTemplate
import os
from ctransformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("marella/gpt-2-ggml", hf=True)
tokenizer = AutoTokenizer.from_pretrained(model)
access_token = os.getenv("Llama2")
def greet(text):
model = AutoModelForCausalLM.from_pretrained("marella/gpt-2-ggml", hf=True)
tokenizer = AutoTokenizer.from_pretrained(model)
# model = "meta-llama/Llama-2-7b-hf"
# tokenizer = AutoTokenizer.from_pretrained(model, token=access_token)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
max_length=512,
do_sample=False,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
token=access_token
)
llm = HuggingFacePipeline(pipeline = pipeline, model_kwargs = {'temperature':0})
template = """Write a concise summary of the following:
"{text}"
CONCISE SUMMARY:"""
prompt = PromptTemplate(template=template, input_variables=["text"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
return llm_chain.run(text)
with gr.Blocks() as demo:
text = gr.Textbox(label="Text")
summary = gr.Textbox(label="Summary")
greet_btn = gr.Button("Submit")
clear = gr.ClearButton([text, summary])
greet_btn.click(fn=greet, inputs=text, outputs=summary, api_name="greet")
demo.launch() |