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("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()