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import gradio as gr
import torch
import transformers
from transformers import AutoTokenizer
from langchain import LLMChain, HuggingFacePipeline, PromptTemplate
import os
access_token = os.getenv("Llama2")
def greet(token, text):
model = "meta-llama/Llama-2-7b-chat-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=1000,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
token=access_token,
use_safetensors=False
)
llm = HuggingFacePipeline(pipeline = pipeline, model_kwargs = {'temperature':0})
template = """
Write a concise summary of the following text delimited by triple backquotes.
´´´{text}´´´
SUMMARY:
"""
prompt = PromptTemplate(template=template, input_variables=["text"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
summary = llm_chain.run(text)
return summary
with gr.Blocks() as demo:
text = gr.Textbox(label="Text")
summary = gr.Textbox(label="Summary")
greet_btn = gr.Button("Submit")
greet_btn.click(fn=greet, inputs=text, outputs=summary, api_name="greet")
demo.launch() |