app.py

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()