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import os
from dotenv import load_dotenv
import numpy as np
from huggingface_hub import snapshot_download
#from difflib import Differ
import gradio as gr
#from transformers import pipeline
#from llama_cpp import Llama
from ctransformers import AutoModelForCausalLM
load_dotenv()
task_asr=os.getenv('TASK_TXTGEN')
model_id=os.getenv('MODEL_MISTRAL')
model_file=os.getenv('MODEL_MISTRAL_FILE')
cached_folder=snapshot_download(
repo_id=model_id,
allow_patterns=model_file,
local_dir=None
)
def get_cached_folder():
return cached_folder
llm = AutoModelForCausalLM.from_pretrained(
model_id,
model_file=model_file,
model_type="llama"
# gpu_layers=50
)
B_INST, E_INST = "<s>[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
DEFAULT_SYSTEM_PROMPT = """\
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
Correct the grammar in the user text.
"""
SYSTEM_PROMPT = B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS
def get_prompt(instruction):
prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST
return prompt_template
def improve_grammar(text_input):
#https://huggingface.co/mzbac/mistral-grammar
#https://llama-cpp-python.readthedocs.io/en/latest/
input_prompt = get_prompt(text_input)
text_output = llm(
input_prompt,
temperature=0.0,
# top_p=0.1,
# top_k=40,
# repeat_penalty=1.1,
# max_tokens=2048,
# n_ctx=2048,
# echo=False
)
return text_output
if __name__ == "__main__":
with gr.Blocks() as demo:
mytextinput = gr.Textbox("This is a test box.")
b1 = gr.Button("Click to improve text")
mytextimproved = gr.Textbox(
label="Improved text"
)
b1.click(
fn=improve_grammar,
inputs=mytextinput,
outputs=mytextimproved
)
demo.queue()
demo.launch(
share=False,
server_name=os.getenv('GRADIO_SERVER_IP'),
server_port=int(os.getenv('GRADIO_SERVER_PORT'))
) |