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import gradio as gr
import json
from nltk.tokenize import sent_tokenize
import torch
import ujson as json
from transformers import AutoModelForCausalLM,LlamaTokenizer
from peft import PeftModel
from keybert import KeyBERT
from keyphrase_vectorizers import KeyphraseCountVectorizer
import nltk
nltk.download('punkt')
# loads Guanaco 7B model - takes around 2-3 minutes - can do this separately
model_name = "llama-7b-hf"
adapters_name = 'guanaco-7b'
# print(f"Starting to load the model {model_name} into memory")
m = AutoModelForCausalLM.from_pretrained(
model_name,
#load_in_4bit=True,
torch_dtype=torch.bfloat16,
device_map='auto'
)
m = PeftModel.from_pretrained(m, adapters_name)
m = m.merge_and_unload()
tok = LlamaTokenizer.from_pretrained(model_name)
tok.bos_token_id = 1
stop_token_ids = [0]
# print(f"Successfully loaded the model {model_name} into memory")
print('Guanaco model loaded into memory.')
def generate(title, abstract):
print("Started running.")
'''
Take gradio input and output data to sample-data.jsonl in readable form for classifier.py to run.
'''
newline = {}
text = abstract
# eliminate word lowercase "abstract" or "abstract." at beginning of abstract text
if text.lower()[0:9] == "abstract.":
text = text[9:]
elif text.lower()[0:8] == "abstract":
text = text[8:]
sentences = sent_tokenize(text)
newline["target"] = sentences
newline["title"] = title
first_file = open("data/sample-data.jsonl", "w")
first_file.write(json.dumps(newline))
first_file.close()
print(newline)
print("Tokenized abstract to sentences.")
'''
Main part
'''
'''
This is for summarization
'''
tooShortForKeyword = False
with open("data/sample-data.jsonl", "r") as f:
obj = [json.loads(l) for l in f]
doc = ""
if len(obj[0]["target"]) > 1:
doc += obj[0]["title"] + ". " + obj[0]["target"][0] + " " + obj[0]["target"][1]
elif len(obj[0]["target"]) == 1:
tooShortForKeyword = True
doc += obj[0]["title"] + ". " + obj[0]["target"][0]
else:
tooShortForKeyword = True
doc += obj[0]["title"]
text = doc
prompt = """
Can you explain the main idea of what is being studied in the following paragraph for someone who is not familiar with the topic. Comment on areas of application.:
"""
formatted_prompt = (
f"A chat between a curious human and an artificial intelligence assistant."
f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
f"### Human: {prompt + doc} \n"
f"### Assistant:"
)
inputs = tok(formatted_prompt, return_tensors="pt").to("cuda:1")
outputs = m.generate(inputs=inputs.input_ids, max_new_tokens=300)
output = tok.decode(outputs[0], skip_special_tokens=True)
index_response = output.find("### Assistant: ") + 15
if (output[index_response:index_response + 10] == "Certainly!"):
index_response += 10
end_response = output.rfind('.') + 1
response = output[index_response:end_response]
with open("data/guanacoSummaryOutput.txt", "w") as f2:
f2.write(response)
print('Plain Language Summary Created.')
'''
Keyphrase extraction.
'''
# the document is the title and first two sentences of the abstract.
with open("data/sample-data.jsonl", "r") as f:
obj = [json.loads(l) for l in f]
doc = ""
if len(obj[0]["target"]) > 1:
doc += obj[0]["title"] + ". " + obj[0]["target"][0] + " " + obj[0]["target"][1]
kw_model = KeyBERT(model="all-MiniLM-L6-v2")
vectorizer = KeyphraseCountVectorizer()
top_n = 2
keywords = kw_model.extract_keywords(doc, stop_words="english", top_n = top_n, vectorizer=vectorizer, use_mmr=True)
my_keywords = []
for i in range(top_n):
add = True
for j in range(top_n):
if i != j:
if keywords[i][0] in keywords[j][0]:
add = False
if add:
my_keywords.append(keywords[i][0])
for entry in my_keywords:
print(entry)
'''
This is for feeding the keyphrases into Guanaco.
'''
responseTwo = ""
keyword_string = ""
if not tooShortForKeyword:
separator = ', '
keyword_string = separator.join(my_keywords)
prompt = "What is the purpose of studying " + keyword_string + "? Comment on areas of application."
formatted_prompt = (
f"A chat between a curious human and an artificial intelligence assistant."
f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
f"### Human: {prompt} \n"
f"### Assistant:"
)
inputs = tok(formatted_prompt, return_tensors="pt").to("cuda:2")
outputs = m.generate(inputs=inputs.input_ids, max_new_tokens=300)
output = tok.decode(outputs[0], skip_special_tokens=True)
index_response = output.find("### Assistant: ") + 15
end_response = output.rfind('.') + 1
responseTwo = output[index_response:end_response]
with open("data/guanacoElaborationOutput.txt", "w") as f2:
f2.write(responseTwo)
print('Keyphrase elaboration ran.')
return keyword_string, responseTwo, response
demo = gr.Interface(
fn=generate,
inputs=[gr.Textbox(label="Title"), gr.Textbox(label="Abstract")],
outputs=[gr.Textbox(label="Keyphrases"), gr.Textbox(label="Keyphrase Elaboration"), gr.Textbox(label="Plain Language Summary")],
).launch(share = True)
print('after launch') # now executes
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