app.py

# # # 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 + " </s>"
#         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

def clean_text(text):
    return text.replace("paraphrasedoutput: ", "")

@spaces.GPU
def generate_paraphrases(text, setting, output_format):
    sentences = splitter.split(text)
    all_sentence_paraphrases = []
    
    if setting == 1:
        num_return_sequences = 5
        temperature = 1.0
        top_k = 50
        top_p = 0.95
        max_length = 128
    elif setting == 2:
        num_return_sequences = 7
        temperature = 1.2
        top_k = 50
        top_p = 0.95
        max_length = 192
    elif setting == 3:
        num_return_sequences = 10
        temperature = 1.4
        top_k = 50
        top_p = 0.95
        max_length = 256
    elif setting == 4:
        num_return_sequences = 15
        temperature = 1.6
        top_k = 50
        top_p = 0.95
        max_length = 320
    else:
        num_return_sequences = 20
        temperature = 1.8
        top_k = 50
        top_p = 0.95
        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 + " </s>"
        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()
        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(output, skip_special_tokens=True, clean_up_tokenization_spaces=True) for output in 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])

def clean_paraphrased_output(text):
    cleaned_text = clean_text(text)
    return cleaned_text

# Define the Gradio interface
iface = gr.Interface(
    fn=clean_paraphrased_output,
    inputs=[
        gr.Textbox(lines=5, label="Input Text")
    ],
    outputs=[
        gr.Textbox(lines=20, label="Cleaned Text")
    ],
    title="Clean Paraphrased Output",
    description="Enter a text with 'paraphrasedoutput:' prefix and get the cleaned text."
)

# Launch the interface
iface.launch()