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from encodings.utf_8 import encode
from openai import OpenAI
import time
from transformers import pipeline
def use_openai(input):
client = OpenAI()
response = client.responses.create(
model="o3-mini-2025-01-31",
# model="gpt-4.1",
input=f"""You are an helpful assistant. Analyze the following transcript and
give a summary of the conversation. Also give the sentiment of each speaker
as the conversation progresses. Also replace Speaker_0x with name or title
if it can be derived from conversation. Response should only contain ascii
characters. {input}"""
)
return response.output_text
from transformers import BigBirdPegasusForConditionalGeneration, AutoTokenizer
def use_bigbird_pegasus_large_arxiv(input):
tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
# by default encoder-attention is `block_sparse` with num_random_blocks=3, block_size=64
model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-arxiv")
# decoder attention type can't be changed & will be "original_full"
# you can change `attention_type` (encoder only) to full attention like this:
model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-arxiv", attention_type="original_full")
# you can change `block_size` & `num_random_blocks` like this:
model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-arxiv", block_size=16, num_random_blocks=2)
input=f"""You are an helpful assistant. Analyze the following transcript and
give a summary of the conversation. Also give the sentiment of each speaker
as the conversation progresses. Also replace Speaker_0x with name or title
if it can be derived from conversation. Response should only contain ascii
characters. {input}"""
inputs = tokenizer(input, return_tensors='pt')
prediction = model.generate(**inputs)
prediction = tokenizer.batch_decode(prediction)
import torch
def use_huggingface(input):
# Define the model name
# model_name = "cnicu/t5-small-booksum"
# model_name = "sshleifer/distilbart-cnn-12-6"
#model_name = "facebook/bart-large-cnn"
model_name = "huggyllama/llama-7b"
#model_name = "google/pegasus-large"
# summarizer = pipeline(task="summarization", model=model_name, device_map="cuda")
summarizer = pipeline(task="summarization", model=model_name,
torch_dtype=torch.float16)
max_length = 1024
#input = "summarize the following transcript:"+input
# length = len(input)
# if length < 1024:
# max_length = length
# else:
# input = input[0:1023]
input = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.
A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.
Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other.
In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage.
Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the
2010 marriage license application, according to court documents.
Prosecutors said the marriages were part of an immigration scam.
On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.
After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective
Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.
All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.
Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.
Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.
The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s
Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.
Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.
If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18.
"""
# # Pass the long text to the model to summarize it with a maximum length of 50 tokens
outputs = summarizer(input, max_length=max_length, do_sample=False)
# , min_length = 25, max_length=500
# Access and print the summarized text in the outputs variable
return outputs[0]['summary_text']
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
def use_huggingface2(input):
# Define the model name
# model_name = "cnicu/t5-small-booksum"
# model_name = "sshleifer/distilbart-cnn-12-6"
#model_name = "facebook/bart-large-cnn"
model_name = "huggyllama/llama-7b"
#model_name = "google/pegasus-large"
quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
quantization_config=quantization_config
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_ids = tokenizer(input, return_tensors="pt").to("cuda")
output = model.generate(**input_ids, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
def transcript_analysis(transcript):
results = []
#print(os.environ['OPENAI_API_KEY'])
input=""
for speaker in transcript:
input += speaker + "\n"
start = time.time()
# response = use_huggingface2(input)
response = use_openai(input)
#response = use_bigbird_pegasus_large_arxiv(input)
stop = time.time()
elapsed=stop-start
print("transcript analysis consumed "+str(elapsed))
return response
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