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# import os
# import json
# import gradio as gr
# import spaces
# import torch
# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
# from sentence_splitter import SentenceSplitter
# from itertools import product
# # Get the Hugging Face token from environment variable
# hf_token = os.getenv('HF_TOKEN')
# cuda_available = torch.cuda.is_available()
# device = torch.device("cuda" if cuda_available else "cpu")
# print(f"Using device: {device}")
# # Initialize paraphraser model and tokenizer
# paraphraser_model_name = "NoaiGPT/777"
# paraphraser_tokenizer = AutoTokenizer.from_pretrained(paraphraser_model_name, use_auth_token=hf_token)
# paraphraser_model = AutoModelForSeq2SeqLM.from_pretrained(paraphraser_model_name, use_auth_token=hf_token).to(device)
# # Initialize classifier model and tokenizer
# classifier_model_name = "andreas122001/roberta-mixed-detector"
# classifier_tokenizer = AutoTokenizer.from_pretrained(classifier_model_name)
# classifier_model = AutoModelForSequenceClassification.from_pretrained(classifier_model_name).to(device)
# # Initialize sentence splitter
# splitter = SentenceSplitter(language='en')
# def classify_text(text):
# inputs = classifier_tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(device)
# with torch.no_grad():
# outputs = classifier_model(**inputs)
# probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
# predicted_class = torch.argmax(probabilities, dim=-1).item()
# main_label = classifier_model.config.id2label[predicted_class]
# main_score = probabilities[0][predicted_class].item()
# return main_label, main_score
# @spaces.GPU
# def generate_paraphrases(text, setting, output_format):
# sentences = splitter.split(text)
# all_sentence_paraphrases = []
# if setting == 1:
# num_return_sequences = 5
# repetition_penalty = 1.1
# no_repeat_ngram_size = 2
# temperature = 1.0
# max_length = 128
# elif setting == 2:
# num_return_sequences = 10
# repetition_penalty = 1.2
# no_repeat_ngram_size = 3
# temperature = 1.2
# max_length = 192
# elif setting == 3:
# num_return_sequences = 15
# repetition_penalty = 1.3
# no_repeat_ngram_size = 4
# temperature = 1.4
# max_length = 256
# elif setting == 4:
# num_return_sequences = 20
# repetition_penalty = 1.4
# no_repeat_ngram_size = 5
# temperature = 1.6
# max_length = 320
# else:
# num_return_sequences = 25
# repetition_penalty = 1.5
# no_repeat_ngram_size = 6
# temperature = 1.8
# max_length = 384
# top_k = 50
# top_p = 0.95
# length_penalty = 1.0
# formatted_output = "Original text:\n" + text + "\n\n"
# formatted_output += "Paraphrased versions:\n"
# json_output = {
# "original_text": text,
# "paraphrased_versions": [],
# "combined_versions": [],
# "human_like_versions": []
# }
# for i, sentence in enumerate(sentences):
# inputs = paraphraser_tokenizer(f'{sentence}', return_tensors="pt", padding="longest", truncation=True, max_length=max_length).to(device)
# # Generate paraphrases using the specified parameters
# outputs = paraphraser_model.generate(
# inputs.input_ids,
# attention_mask=inputs.attention_mask,
# num_return_sequences=num_return_sequences,
# repetition_penalty=repetition_penalty,
# no_repeat_ngram_size=no_repeat_ngram_size,
# temperature=temperature,
# max_length=max_length,
# top_k=top_k,
# top_p=top_p,
# do_sample=True,
# early_stopping=False,
# length_penalty=length_penalty
# )
# paraphrases = paraphraser_tokenizer.batch_decode(outputs, skip_special_tokens=True)
# formatted_output += f"Original sentence {i+1}: {sentence}\n"
# for j, paraphrase in enumerate(paraphrases, 1):
# formatted_output += f" Paraphrase {j}: {paraphrase}\n"
# json_output["paraphrased_versions"].append({
# f"original_sentence_{i+1}": sentence,
# "paraphrases": paraphrases
# })
# all_sentence_paraphrases.append(paraphrases)
# formatted_output += "\n"
# all_combinations = list(product(*all_sentence_paraphrases))
# formatted_output += "\nCombined paraphrased versions:\n"
# combined_versions = []
# for i, combination in enumerate(all_combinations[:50], 1): # Limit to 50 combinations
# combined_paraphrase = " ".join(combination)
# combined_versions.append(combined_paraphrase)
# json_output["combined_versions"] = combined_versions
# # Classify combined versions
# human_versions = []
# for i, version in enumerate(combined_versions, 1):
# label, score = classify_text(version)
# formatted_output += f"Version {i}:\n{version}\n"
# formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
# if label == "human-produced" or (label == "machine-generated" and score < 0.98):
# human_versions.append((version, label, score))
# formatted_output += "\nHuman-like or Less Confident Machine-generated versions:\n"
# for i, (version, label, score) in enumerate(human_versions, 1):
# formatted_output += f"Version {i}:\n{version}\n"
# formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
# json_output["human_like_versions"] = [
# {"version": version, "label": label, "confidence_score": score}
# for version, label, score in human_versions
# ]
# # If no human-like versions, include the top 5 least confident machine-generated versions
# if not human_versions:
# human_versions = sorted([(v, l, s) for v, l, s in zip(combined_versions, [classify_text(v)[0] for v in combined_versions], [classify_text(v)[1] for v in combined_versions])], key=lambda x: x[2])[:5]
# formatted_output += "\nNo human-like versions found. Showing top 5 least confident machine-generated versions:\n"
# for i, (version, label, score) in enumerate(human_versions, 1):
# formatted_output += f"Version {i}:\n{version}\n"
# formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
# if output_format == "text":
# return formatted_output, "\n\n".join([v[0] for v in human_versions])
# else:
# return json.dumps(json_output, indent=2), "\n\n".join([v[0] for v in human_versions])
# # Define the Gradio interface
# iface = gr.Interface(
# fn=generate_paraphrases,
# inputs=[
# gr.Textbox(lines=5, label="Input Text"),
# gr.Slider(minimum=1, maximum=5, step=1, label="Readability to Human-like Setting"),
# gr.Radio(["text", "json"], label="Output Format")
# ],
# outputs=[
# gr.Textbox(lines=20, label="Detailed Paraphrases and Classifications"),
# gr.Textbox(lines=10, label="Human-like or Less Confident Machine-generated Paraphrases")
# ],
# title="Advanced Diverse Paraphraser with Human-like Filter",
# description="Enter a text, select a setting from readable to human-like, and choose the output format to generate diverse paraphrased versions. Combined versions are classified, and those detected as human-produced or less confidently machine-generated are presented in the final output."
# )
# # Launch the interface
# iface.launch()
import os
import json
import gradio as gr
import spaces
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, T5ForConditionalGeneration
from sentence_splitter import SentenceSplitter
from itertools import product
# Get the Hugging Face token from environment variable
hf_token = os.getenv('HF_TOKEN')
cuda_available = torch.cuda.is_available()
device = torch.device("cuda" if cuda_available else "cpu")
print(f"Using device: {device}")
# Initialize paraphraser model and tokenizer
paraphraser_model_name = "NoaiGPT/777"
paraphraser_tokenizer = AutoTokenizer.from_pretrained(paraphraser_model_name, use_auth_token=hf_token)
paraphraser_model = AutoModelForSeq2SeqLM.from_pretrained(paraphraser_model_name, use_auth_token=hf_token).to(device)
# Initialize classifier model and tokenizer
classifier_model_name = "andreas122001/roberta-mixed-detector"
classifier_tokenizer = AutoTokenizer.from_pretrained(classifier_model_name)
classifier_model = AutoModelForSequenceClassification.from_pretrained(classifier_model_name).to(device)
# Initialize grammar correction model and tokenizer
grammar_model_name = "grammarly/coedit-large"
grammar_tokenizer = AutoTokenizer.from_pretrained(grammar_model_name)
grammar_model = T5ForConditionalGeneration.from_pretrained(grammar_model_name).to(device)
# Initialize sentence splitter
splitter = SentenceSplitter(language='en')
def classify_text(text):
inputs = classifier_tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(device)
with torch.no_grad():
outputs = classifier_model(**inputs)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
predicted_class = torch.argmax(probabilities, dim=-1).item()
main_label = classifier_model.config.id2label[predicted_class]
main_score = probabilities[0][predicted_class].item()
return main_label, main_score
@spaces.GPU
def correct_grammar(text):
inputs = grammar_tokenizer(f'Fix grammatical errors in this sentence: {text}', return_tensors="pt").input_ids.to(device)
outputs = grammar_model.generate(inputs, max_length=256)
corrected_text = grammar_tokenizer.decode(outputs[0], skip_special_tokens=True)
print(corrected_text)
return corrected_text
@spaces.GPU
def generate_paraphrases(text, setting, output_format):
sentences = splitter.split(text)
all_sentence_paraphrases = []
if setting == 1:
num_return_sequences = 2
repetition_penalty = 1.1
no_repeat_ngram_size = 2
temperature = 1.0
max_length = 128
elif setting == 2:
num_return_sequences = 2
repetition_penalty = 1.2
no_repeat_ngram_size = 3
temperature = 1.2
max_length = 192
elif setting == 3:
# num_return_sequences = 15
num_return_sequences = 2
repetition_penalty = 1.3
no_repeat_ngram_size = 4
temperature = 1.4
max_length = 256
elif setting == 4:
num_return_sequences = 2
repetition_penalty = 1.4
no_repeat_ngram_size = 5
temperature = 1.6
max_length = 320
else:
num_return_sequences = 2
repetition_penalty = 1.5
no_repeat_ngram_size = 6
temperature = 1.8
max_length = 384
top_k = 50
top_p = 0.95
length_penalty = 1.0
formatted_output = "Original text:\n" + text + "\n\n"
formatted_output += "Paraphrased versions:\n"
json_output = {
"original_text": text,
"paraphrased_versions": [],
"combined_versions": [],
"human_like_versions": []
}
for i, sentence in enumerate(sentences):
inputs = paraphraser_tokenizer(f'paraphraser: {sentence}', return_tensors="pt", padding="longest", truncation=True, max_length=max_length).to(device)
# Generate paraphrases using the specified parameters
outputs = paraphraser_model.generate(
inputs.input_ids,
attention_mask=inputs.attention_mask,
num_return_sequences=num_return_sequences,
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
temperature=temperature,
max_length=max_length,
top_k=top_k,
top_p=top_p,
do_sample=True,
early_stopping=False,
length_penalty=length_penalty
)
paraphrases = paraphraser_tokenizer.batch_decode(outputs, skip_special_tokens=True)
corrected_paraphrases = [correct_grammar(paraphrase) for paraphrase in paraphrases]
formatted_output += f"Original sentence {i+1}: {sentence}\n"
for j, paraphrase in enumerate(corrected_paraphrases, 1):
formatted_output += f" Paraphrase {j}: {paraphrase}\n"
json_output["paraphrased_versions"].append({
f"original_sentence_{i+1}": sentence,
"paraphrases": corrected_paraphrases
})
all_sentence_paraphrases.append(corrected_paraphrases)
formatted_output += "\n"
all_combinations = list(product(*all_sentence_paraphrases))
formatted_output += "\nCombined paraphrased versions:\n"
combined_versions = []
for i, combination in enumerate(all_combinations[:50], 1): # Limit to 50 combinations
combined_paraphrase = " ".join(combination)
combined_versions.append(combined_paraphrase)
json_output["combined_versions"] = combined_versions
# # Classify combined versions
# human_versions = []
# for i, version in enumerate(combined_versions, 1):
# label, score = classify_text(version)
# formatted_output += f"Version {i}:\n{version}\n"
# formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
# if label == "human-produced" or (label == "machine-generated" and score < 0.98):
# human_versions.append((version, label, score))
# formatted_output += "\nHuman-like or Less Confident Machine-generated versions:\n"
# for i, (version, label, score) in enumerate(human_versions, 1):
# formatted_output += f"Version {i}:\n{version}\n"
# formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
# json_output["human_like_versions"] = [
# {"version": version, "label": label, "confidence_score": score}
# for version, label, score in human_versions
# ]
# # If no human-like versions, include the top 5 least confident machine-generated versions
# if not human_versions:
# human_versions = sorted([(v, l, s) for v, l, s in zip(combined_versions, [classify_text(v)[0] for v in combined_versions], [classify_text(v)[1] for v in combined_versions])], key=lambda x: x[2])[:5]
# formatted_output += "\nNo human-like versions found. Showing top 5 least confident machine-generated versions:\n"
# for i, (version, label, score) in enumerate(human_versions, 1):
# formatted_output += f"Version {i}:\n{version}\n"
# formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
# if output_format == "text":
# return formatted_output, "\n\n".join([v[0] for v in human_versions])
# else:
# return json.dumps(json_output, indent=2), "\n\n".join([v[0] for v in human_versions])
# # Define the Gradio interface
# iface = gr.Interface(
# fn=generate_paraphrases,
# inputs=[
# gr.Textbox(lines=5, label="Input Text"),
# gr.Slider(minimum=1, maximum=5, step=1, label="Readability to Human-like Setting"),
# gr.Radio(["text", "json"], label="Output Format")
# ],
# outputs=[
# gr.Textbox(lines=20, label="Detailed Paraphrases and Classifications"),
# gr.Textbox(lines=10, label="Human-like or Less Confident Machine-generated Paraphrases")
# ],
# title="Advanced Diverse Paraphraser with Human-like Filter",
# description="Enter a text, select a setting from readable to human-like, and choose the output format to generate diverse paraphrased versions. Combined versions are classified, and those detected as human-produced or less confidently machine-generated are presented in the final output."
# )
# # Launch the interface
# iface.launch()
import os
import json
import gradio as gr
import spaces
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, T5ForConditionalGeneration
from sentence_splitter import SentenceSplitter
from itertools import product
# Get the Hugging Face token from environment variable
hf_token = os.getenv('HF_TOKEN')
cuda_available = torch.cuda.is_available()
device = torch.device("cuda" if cuda_available else "cpu")
print(f"Using device: {device}")
# Initialize paraphraser model and tokenizer
paraphraser_model_name = "NoaiGPT/777"
paraphraser_tokenizer = AutoTokenizer.from_pretrained(paraphraser_model_name, use_auth_token=hf_token)
paraphraser_model = AutoModelForSeq2SeqLM.from_pretrained(paraphraser_model_name, use_auth_token=hf_token).to(device)
# Initialize classifier model and tokenizer
classifier_model_name = "andreas122001/roberta-mixed-detector"
classifier_tokenizer = AutoTokenizer.from_pretrained(classifier_model_name)
classifier_model = AutoModelForSequenceClassification.from_pretrained(classifier_model_name).to(device)
# Initialize grammar correction model and tokenizer
grammar_model_name = "grammarly/coedit-large"
grammar_tokenizer = AutoTokenizer.from_pretrained(grammar_model_name)
grammar_model = T5ForConditionalGeneration.from_pretrained(grammar_model_name).to(device)
# Initialize sentence splitter
splitter = SentenceSplitter(language='en')
def classify_text(text):
inputs = classifier_tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(device)
with torch.no_grad():
outputs = classifier_model(**inputs)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
predicted_class = torch.argmax(probabilities, dim=-1).item()
main_label = classifier_model.config.id2label[predicted_class]
main_score = probabilities[0][predicted_class].item()
return main_label, main_score
@spaces.GPU
def correct_grammar(text):
inputs = grammar_tokenizer(f'Fix grammatical errors in this sentence: {text}', return_tensors="pt").input_ids.to(device)
outputs = grammar_model.generate(inputs, max_length=256)
corrected_text = grammar_tokenizer.decode(outputs[0], skip_special_tokens=True)
return corrected_text
@spaces.GPU
def generate_paraphrases(text, setting, output_format):
sentences = splitter.split(text)
all_sentence_paraphrases = []
# Define settings
settings = {
1: {"num_return_sequences": 2, "repetition_penalty": 1.1, "no_repeat_ngram_size": 2, "temperature": 1.0, "max_length": 128},
2: {"num_return_sequences": 2, "repetition_penalty": 1.2, "no_repeat_ngram_size": 3, "temperature": 1.2, "max_length": 192},
3: {"num_return_sequences": 2, "repetition_penalty": 1.3, "no_repeat_ngram_size": 4, "temperature": 1.4, "max_length": 256},
4: {"num_return_sequences": 2, "repetition_penalty": 1.4, "no_repeat_ngram_size": 5, "temperature": 1.6, "max_length": 320},
5: {"num_return_sequences": 2, "repetition_penalty": 1.5, "no_repeat_ngram_size": 6, "temperature": 1.8, "max_length": 384}
}
config = settings.get(setting, settings[5])
top_k = 50
top_p = 0.95
length_penalty = 1.0
formatted_output = "Original text:\n" + text + "\n\n"
formatted_output += "Paraphrased versions:\n"
json_output = {
"original_text": text,
"paraphrased_versions": [],
"combined_versions": [],
"human_like_versions": []
}
# Process sentences in batches
batch_size = 4
for i in range(0, len(sentences), batch_size):
batch_sentences = sentences[i:i + batch_size]
inputs = paraphraser_tokenizer([f'paraphraser: {sentence}' for sentence in batch_sentences], return_tensors="pt", padding="longest", truncation=True, max_length=config["max_length"]).to(device)
# Generate paraphrases using the specified parameters
outputs = paraphraser_model.generate(
inputs.input_ids,
attention_mask=inputs.attention_mask,
num_return_sequences=config["num_return_sequences"],
repetition_penalty=config["repetition_penalty"],
no_repeat_ngram_size=config["no_repeat_ngram_size"],
temperature=config["temperature"],
max_length=config["max_length"],
top_k=top_k,
top_p=top_p,
do_sample=True,
early_stopping=False,
length_penalty=length_penalty
)
paraphrases = paraphraser_tokenizer.batch_decode(outputs, skip_special_tokens=True)
corrected_paraphrases = [correct_grammar(paraphrase) for paraphrase in paraphrases]
for j, sentence in enumerate(batch_sentences):
formatted_output += f"Original sentence {i + j + 1}: {sentence}\n"
sentence_paraphrases = corrected_paraphrases[j * config["num_return_sequences"]:(j + 1) * config["num_return_sequences"]]
for k, paraphrase in enumerate(sentence_paraphrases, 1):
formatted_output += f" Paraphrase {k}: {paraphrase}\n"
json_output["paraphrased_versions"].append({
f"original_sentence_{i + j + 1}": sentence,
"paraphrases": sentence_paraphrases
})
all_sentence_paraphrases.append(sentence_paraphrases)
formatted_output += "\n"
all_combinations = list(product(*all_sentence_paraphrases))
formatted_output += "\nCombined paraphrased versions:\n"
combined_versions = []
for i, combination in enumerate(all_combinations[:50], 1): # Limit to 50 combinations
combined_paraphrase = " ".join(combination)
combined_versions.append(combined_paraphrase)
json_output["combined_versions"] = combined_versions
# Classify combined versions
human_versions = []
for i, version in enumerate(combined_versions, 1):
label, score = classify_text(version)
formatted_output += f"Version {i}:\n{version}\n"
formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
if label == "human-produced" or (label == "machine-generated" and score < 0.98):
human_versions.append((version, label, score))
formatted_output += "\nHuman-like or Less Confident Machine-generated versions:\n"
for i, (version, label, score) in enumerate(human_versions, 1):
formatted_output += f"Version {i}:\n{version}\n"
formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
json_output["human_like_versions"] = [
{"version": version, "label": label, "confidence_score": score}
for version, label, score in human_versions
]
# If no human-like versions, include the top 5 least confident machine-generated versions
if not human_versions:
human_versions = sorted([(v, l, s) for v, l, s in zip(combined_versions, [classify_text(v)[0] for v in combined_versions], [classify_text(v)[1] for v in combined_versions])], key=lambda x: x[2])[:5]
formatted_output += "\nNo human-like versions found. Showing top 5 least confident machine-generated versions:\n"
for i, (version, label, score) in enumerate(human_versions, 1):
formatted_output += f"Version {i}:\n{version}\n"
formatted_output += f"Classification: {label} (confidence: {score:.2%})\n\n"
if output_format == "text":
return formatted_output, "\n\n".join([v[0] for v in human_versions])
else:
return json.dumps(json_output, indent=2), "\n\n".join([v[0] for v in human_versions])
# Define the Gradio interface
iface = gr.Interface(
fn=generate_paraphrases,
inputs=[
gr.Textbox(lines=5, label="Input Text"),
gr.Slider(minimum=1, maximum=5, step=1, label="Readability to Human-like Setting"),
gr.Radio(["text", "json"], label="Output Format")
],
outputs=[
gr.Textbox(lines=20, label="Detailed Paraphrases and Classifications"),
gr.Textbox(lines=10, label="Human-like or Less Confident Machine-generated Paraphrases")
],
title="Advanced Diverse Paraphraser with Human-like Filter",
description="Enter a text, select a setting from readable to human-like, and choose the output format to generate diverse paraphrased versions. Combined versions are classified, and those detected as human-produced or less confidently machine-generated are presented in the final output."
)
# Launch the interface
iface.launch() |