# # # 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("cpu" 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'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) # # # 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 pipeline, 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 = "sharad/ParaphraseGPT" # # paraphraser_tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base") # # paraphraser_model = AutoModelForSeq2SeqLM.from_pretrained(paraphraser_model_name).to(device) # # paraphrase_pipeline = pipeline("text2text-generation", model=paraphraser_model, tokenizer=paraphraser_tokenizer, device=0 if cuda_available else -1) # # # 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 = 0.9 # # max_length = 128 # # elif setting == 2: # # num_return_sequences = 5 # # repetition_penalty = 1.2 # # no_repeat_ngram_size = 3 # # temperature = 0.95 # # max_length = 192 # # elif setting == 3: # # num_return_sequences = 5 # # repetition_penalty = 1.3 # # no_repeat_ngram_size = 4 # # temperature = 1.0 # # max_length = 256 # # elif setting == 4: # # num_return_sequences = 5 # # repetition_penalty = 1.4 # # no_repeat_ngram_size = 5 # # temperature = 1.05 # # max_length = 320 # # else: # # num_return_sequences = 5 # # repetition_penalty = 1.5 # # no_repeat_ngram_size = 6 # # temperature = 1.1 # # 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): # # paraphrases = paraphrase_pipeline( # # sentence, # # num_return_sequences=num_return_sequences, # # do_sample=True, # # top_k=top_k, # # top_p=top_p, # # temperature=temperature, # # no_repeat_ngram_size=no_repeat_ngram_size, # # repetition_penalty=repetition_penalty, # # max_length=max_length # # ) # # paraphrases_texts = [p['generated_text'] for p in paraphrases] # # formatted_output += f"Original sentence {i+1}: {sentence}\n" # # for j, paraphrase in enumerate(paraphrases_texts, 1): # # formatted_output += f" Paraphrase {j}: {paraphrase}\n" # # json_output["paraphrased_versions"].append({ # # f"original_sentence_{i+1}": sentence, # # "paraphrases": paraphrases_texts # # }) # # all_sentence_paraphrases.append(paraphrases_texts) # # 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 # import sys # import subprocess # from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification # from sentence_splitter import SentenceSplitter # from itertools import product # # Ensure sentencepiece is installed # try: # import sentencepiece # except ImportError: # subprocess.check_call([sys.executable, "-m", "pip", "install", "sentencepiece"]) # # 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 = "ramsrigouthamg/t5-large-paraphraser-diverse-high-quality" # paraphraser_tokenizer = AutoTokenizer.from_pretrained(paraphraser_model_name, use_fast=False) # paraphraser_model = AutoModelForSeq2SeqLM.from_pretrained(paraphraser_model_name).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 = 3 # num_beams = 5 # max_length = 128 # elif setting == 2: # num_return_sequences = 3 # num_beams = 7 # max_length = 192 # elif setting == 3: # num_return_sequences = 3 # num_beams = 9 # max_length = 256 # elif setting == 4: # num_return_sequences = 3 # num_beams = 11 # max_length = 320 # else: # num_return_sequences = 3 # num_beams = 15 # max_length = 384 # 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): # text = "paraphrase: " + sentence + " " # encoding = paraphraser_tokenizer.encode_plus(text, max_length=max_length, padding=True, return_tensors="pt") # input_ids, attention_mask = encoding["input_ids"].to(device), encoding["attention_mask"].to(device) # paraphraser_model.eval() # beam_outputs = paraphraser_model.generate( # input_ids=input_ids, # attention_mask=attention_mask, # max_length=max_length, # early_stopping=True, # num_beams=num_beams, # num_return_sequences=num_return_sequences # ) # paraphrases_texts = [paraphraser_tokenizer.decode(beam_output, skip_special_tokens=True, clean_up_tokenization_spaces=True) for beam_output in beam_outputs] # formatted_output += f"Original sentence {i+1}: {sentence}\n" # for j, paraphrase in enumerate(paraphrases_texts, 1): # formatted_output += f" Paraphrase {j}: {paraphrase}\n" # json_output["paraphrased_versions"].append({ # f"original_sentence_{i+1}": sentence, # "paraphrases": paraphrases_texts # }) # all_sentence_paraphrases.append(paraphrases_texts) # 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 < 0.90): # Adjusted threshold # 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 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 = "ramsrigouthamg/t5-large-paraphraser-diverse-high-quality" paraphraser_tokenizer = AutoTokenizer.from_pretrained(paraphraser_model_name) paraphraser_model = AutoModelForSeq2SeqLM.from_pretrained(paraphraser_model_name).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 = 3 temperature = 0.7 top_k = 50 top_p = 0.9 max_length = 128 elif setting == 2: num_return_sequences = 3 temperature = 0.8 top_k = 50 top_p = 0.9 max_length = 192 elif setting == 3: num_return_sequences = 3 temperature = 0.9 top_k = 50 top_p = 0.9 max_length = 256 elif setting == 4: num_return_sequences = 3 temperature = 1.0 top_k = 50 top_p = 0.9 max_length = 320 else: num_return_sequences = 3 temperature = 1.1 top_k = 50 top_p = 0.9 max_length = 384 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): text = "paraphrase: " + sentence + " " encoding = paraphraser_tokenizer.encode_plus(text, max_length=max_length, padding=True, return_tensors="pt") input_ids, attention_mask = encoding["input_ids"].to(device), encoding["attention_mask"].to(device) paraphraser_model.eval() beam_outputs = paraphraser_model.generate( input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, num_return_sequences=num_return_sequences, do_sample=True, top_k=top_k, top_p=top_p, temperature=temperature ) paraphrases_texts = [paraphraser_tokenizer.decode(beam_output, skip_special_tokens=True, clean_up_tokenization_spaces=True) for beam_output in beam_outputs] formatted_output += f"Original sentence {i+1}: {sentence}\n" for j, paraphrase in enumerate(paraphrases_texts, 1): formatted_output += f" Paraphrase {j}: {paraphrase}\n" json_output["paraphrased_versions"].append({ f"original_sentence_{i+1}": sentence, "paraphrases": paraphrases_texts }) all_sentence_paraphrases.append(paraphrases_texts) 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 < 0.90): # Adjusted threshold 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()