app.py

��import gradio as gr
import torch
from transformers import (
    AutoTokenizer, 
    AutoModelForSeq2SeqLM,
    T5ForConditionalGeneration,
    T5Tokenizer
)
from sentence_transformers import SentenceTransformer, util
import numpy as np
from typing import List, Tuple, Dict
import re
import difflib

# Initialize similarity model
similarity_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# Model configurations
PARAPHRASE_MODELS = {
    "T5-Base": "Vamsi/T5_Paraphrase_Paws",
    "PEGASUS-Paraphrase": "tuner007/pegasus_paraphrase",
    "Parrot-Paraphraser": "prithivida/parrot_paraphraser_on_T5",
    "BART-Paraphrase": "eugenesiow/bart-paraphrase",
}

EXPANSION_MODELS = {
    "Flan-T5-Base": "google/flan-t5-base",
    "Flan-T5-Large": "google/flan-t5-large",
}

# Cache for loaded models
model_cache = {}

def load_model(model_name: str, model_path: str):
    """Load model and tokenizer with caching"""
    if model_name in model_cache:
        return model_cache[model_name]
    
    print(f"Loading {model_name}...")
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
    
    # Move to GPU if available
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model = model.to(device)
    
    model_cache[model_name] = (model, tokenizer, device)
    return model, tokenizer, device

def chunk_text(text: str, max_sentences: int = 4) -> List[str]:
    """Split text into chunks based on number of sentences"""
    import re
    sentences = re.split(r'(?<=[.!?]) +', text.strip())
    chunks = [' '.join(sentences[i:i+max_sentences]) for i in range(0, len(sentences), max_sentences)]
    return [chunk for chunk in chunks if chunk.strip()]

def estimate_tokens(text: str) -> int:
    """Estimate number of tokens in text (approximate: 1 token H" 0.75 words)"""
    word_count = len(text.split())
    return int(word_count / 0.75)

def calculate_max_length(input_text: str, mode: str, base_max_length: int) -> int:
    """Calculate appropriate max_length based on input tokens"""
    input_tokens = estimate_tokens(input_text)
    
    if mode == "Paraphrase":
        # For paraphrasing: output should be 1.2-1.5x input tokens
        calculated_max = int(input_tokens * 1.5) + 50
    else:
        # For expansion: output should be 2-3x input tokens
        calculated_max = int(input_tokens * 3) + 100
    
    # Use the larger of calculated or user-specified max_length
    final_max_length = max(calculated_max, base_max_length)
    
    # Cap at reasonable maximum to avoid memory issues
    return min(final_max_length, 1024)

def calculate_similarity(text1: str, text2: str) -> float:
    """Calculate cosine similarity between two texts"""
    embeddings = similarity_model.encode([text1, text2], convert_to_tensor=True)
    similarity = util.cos_sim(embeddings[0], embeddings[1])
    return similarity.item()

def highlight_differences(original: str, generated: str) -> Tuple[str, str, Dict]:
    """
    Create highlighted HTML versions of both texts showing differences
    Returns: (highlighted_original, highlighted_generated, statistics)
    """
    # Split into words for comparison
    original_words = original.split()
    generated_words = generated.split()
    
    # Use difflib to find differences
    diff = difflib.SequenceMatcher(None, original_words, generated_words)
    
    highlighted_original = []
    highlighted_generated = []
    
    changes_count = 0
    additions_count = 0
    deletions_count = 0
    unchanged_count = 0
    word_substitutions = []
    
    for tag, i1, i2, j1, j2 in diff.get_opcodes():
        original_chunk = ' '.join(original_words[i1:i2])
        generated_chunk = ' '.join(generated_words[j1:j2])
        
        if tag == 'equal':
            # Unchanged text
            highlighted_original.append(original_chunk)
            highlighted_generated.append(generated_chunk)
            unchanged_count += (i2 - i1)
            
        elif tag == 'replace':
            # Changed text
            highlighted_original.append(f'<span style="background-color: #ffcccc; padding: 2px 4px; border-radius: 3px; text-decoration: line-through;">{original_chunk}</span>')
            highlighted_generated.append(f'<span style="background-color: #ccffcc; padding: 2px 4px; border-radius: 3px; font-weight: 500;">{generated_chunk}</span>')
            changes_count += max(i2 - i1, j2 - j1)
            
            # Track word substitutions (limit to single word changes for clarity)
            if i2 - i1 == 1 and j2 - j1 == 1:
                word_substitutions.append((original_chunk, generated_chunk))
            
        elif tag == 'delete':
            # Text removed in generated
            highlighted_original.append(f'<span style="background-color: #ffcccc; padding: 2px 4px; border-radius: 3px; text-decoration: line-through;">{original_chunk}</span>')
            deletions_count += (i2 - i1)
            
        elif tag == 'insert':
            # Text added in generated
            highlighted_generated.append(f'<span style="background-color: #ccffcc; padding: 2px 4px; border-radius: 3px; font-weight: 500;">{generated_chunk}</span>')
            additions_count += (j2 - j1)
    
    # Join with spaces
    final_original = ' '.join(highlighted_original)
    final_generated = ' '.join(highlighted_generated)
    
    # Calculate statistics
    total_original_words = len(original_words)
    total_generated_words = len(generated_words)
    
    percentage_changed = (changes_count + deletions_count + additions_count) / max(total_original_words, 1) * 100
    percentage_unchanged = (unchanged_count / max(total_original_words, 1)) * 100
    
    statistics = {
        'total_original': total_original_words,
        'total_generated': total_generated_words,
        'unchanged': unchanged_count,
        'changed': changes_count,
        'added': additions_count,
        'deleted': deletions_count,
        'percentage_changed': percentage_changed,
        'percentage_unchanged': percentage_unchanged,
        'substitutions': word_substitutions[:10]  # Limit to first 10
    }
    
    return final_original, final_generated, statistics

def format_statistics(stats: Dict) -> str:
    """Format statistics into a readable HTML string"""
    html = f"""
    <div style="padding: 15px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 10px; color: white; margin: 10px 0;">
        <h3 style="margin-top: 0; color: white;">=��� Change Analysis</h3>
        
        <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); gap: 10px; margin: 15px 0;">
            <div style="background: rgba(255,255,255,0.1); padding: 10px; border-radius: 8px; text-align: center;">
                <div style="font-size: 24px; font-weight: bold;">{stats['total_original']}</div>
                <div style="font-size: 12px; opacity: 0.9;">Original Words</div>
            </div>
            <div style="background: rgba(255,255,255,0.1); padding: 10px; border-radius: 8px; text-align: center;">
                <div style="font-size: 24px; font-weight: bold;">{stats['total_generated']}</div>
                <div style="font-size: 12px; opacity: 0.9;">Generated Words</div>
            </div>
            <div style="background: rgba(255,255,255,0.1); padding: 10px; border-radius: 8px; text-align: center;">
                <div style="font-size: 24px; font-weight: bold; color: #90EE90;">{stats['unchanged']}</div>
                <div style="font-size: 12px; opacity: 0.9;">Unchanged</div>
            </div>
            <div style="background: rgba(255,255,255,0.1); padding: 10px; border-radius: 8px; text-align: center;">
                <div style="font-size: 24px; font-weight: bold; color: #FFB6C1;">{stats['changed']}</div>
                <div style="font-size: 12px; opacity: 0.9;">Changed</div>
            </div>
        </div>
        
        <div style="margin: 15px 0; padding: 10px; background: rgba(255,255,255,0.1); border-radius: 8px;">
            <div style="margin-bottom: 8px;">
                <strong>Modification Rate:</strong> {stats['percentage_changed']:.1f}% modified, {stats['percentage_unchanged']:.1f}% preserved
            </div>
            <div style="margin-bottom: 8px;">
                <span style="color: #90EE90;">' Added: {stats['added']} words</span> | 
                <span style="color: #FFB6C1;">' Removed: {stats['deleted']} words</span>
            </div>
        </div>
    """
    
    if stats['substitutions']:
        html += """
        <div style="margin-top: 15px; padding: 10px; background: rgba(255,255,255,0.1); border-radius: 8px;">
            <strong>=�� Sample Word Substitutions:</strong>
            <div style="margin-top: 8px; font-size: 13px; line-height: 1.6;">
        """
        for orig, new in stats['substitutions']:
            html += f'<div style="margin: 4px 0;"><span style="color: #FFB6C1;">{orig}</span> �! <span style="color: #90EE90;">{new}</span></div>'
        html += """
            </div>
        </div>
        """
    
    html += """
        <div style="margin-top: 15px; padding: 8px; background: rgba(255,255,255,0.05); border-radius: 6px; font-size: 12px;">
            <strong>Legend:</strong> 
            <span style="background-color: #ffcccc; padding: 2px 6px; border-radius: 3px; margin: 0 4px;">Removed/Changed</span>
            <span style="background-color: #ccffcc; padding: 2px 6px; border-radius: 3px; margin: 0 4px;">Added/New</span>
        </div>
    </div>
    """
    
    return html

def paraphrase_text(
    text: str,
    model_name: str,
    temperature: float,
    top_p: float,
    max_length: int,
    num_beams: int,
    max_sentences: int,
    target_words: int = None,
    mode: str = "Paraphrase"
) -> Tuple[str, float]:
    """Paraphrase or expand text based on mode"""
    
    if not text.strip():
        return "Please enter some text to process.", 0.0
    
    # Select appropriate model based on mode
    if mode == "Paraphrase":
        models_dict = PARAPHRASE_MODELS
        if model_name not in models_dict:
            model_name = list(models_dict.keys())[0]
        model_path = models_dict[model_name]
        prefix = "paraphrase: " if "T5" in model_name else ""
    else:  # Expand mode
        models_dict = EXPANSION_MODELS
        if model_name not in models_dict:
            model_name = list(models_dict.keys())[0]
        model_path = models_dict[model_name]
        target_words = target_words or 300
        prefix = f"Expand the following text to approximately {target_words} words, adding more details and context: "
    
    # Load model
    model, tokenizer, device = load_model(model_name, model_path)
    
    # Chunk text based on sentences
    chunks = chunk_text(text, max_sentences=max_sentences)
    
    processed_chunks = []
    
    print(f"\n{'='*60}")
    print(f"Processing {len(chunks)} chunk(s) with {max_sentences} sentences per chunk")
    print(f"{'='*60}")
    
    for i, chunk in enumerate(chunks):
        # Calculate dynamic max_length for this chunk
        chunk_max_length = calculate_max_length(chunk, mode, max_length)
        input_tokens = estimate_tokens(chunk)
        
        # Prepare input
        input_text = prefix + chunk + " </s>" if mode == "Paraphrase" else prefix + chunk
        inputs = tokenizer.encode(
            input_text, 
            return_tensors="pt", 
            max_length=512, 
            truncation=True
        )
        inputs = inputs.to(device)
        
        # Calculate min_length to ensure output isn't too short
        if mode == "Paraphrase":
            min_length_calc = int(input_tokens * 0.8)
        else:
            min_length_calc = int(input_tokens * 1.5)
        
        # Generate
        with torch.no_grad():
            outputs = model.generate(
                inputs,
                max_length=chunk_max_length,
                min_length=min(min_length_calc, chunk_max_length - 10),
                num_beams=num_beams,
                temperature=temperature if temperature > 0 else 1.0,
                top_p=top_p,
                top_k=120 if mode == "Paraphrase" else 50,
                do_sample=temperature > 0,
                early_stopping=True,
                no_repeat_ngram_size=3 if mode == "Expand" else 2,
                length_penalty=1.0 if mode == "Paraphrase" else 1.5,
                repetition_penalty=1.2,
            )
        
        # Decode output
        processed_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        processed_chunks.append(processed_text.strip())
        
        output_tokens = estimate_tokens(processed_text)
        print(f"Chunk {i+1}/{len(chunks)}:")
        print(f"  Input: {len(chunk.split())} words (~{input_tokens} tokens)")
        print(f"  Output: {len(processed_text.split())} words (~{output_tokens} tokens)")
        print(f"  Max length used: {chunk_max_length}")
        print(f"-" * 60)
    
    # Combine chunks with double newline
    final_text = "\n\n".join(processed_chunks)
    
    # Calculate similarity
    similarity_score = calculate_similarity(text, final_text)
    
    print(f"{'='*60}")
    print(f"Total: {len(text.split())} �! {len(final_text.split())} words")
    print(f"Similarity: {similarity_score:.4f}")
    print(f"{'='*60}\n")
    
    return final_text, similarity_score

def update_model_choices(mode: str):
    """Update model dropdown based on selected mode"""
    if mode == "Paraphrase":
        choices = list(PARAPHRASE_MODELS.keys())
    else:
        choices = list(EXPANSION_MODELS.keys())
    return gr.Dropdown(choices=choices, value=choices[0])

def update_parameters_visibility(mode: str):
    """Show/hide target words parameter based on mode"""
    if mode == "Expand":
        return gr.Number(visible=True)
    else:
        return gr.Number(visible=False)

def process_text(
    input_text: str,
    mode: str,
    model_name: str,
    temperature: float,
    top_p: float,
    max_length: int,
    num_beams: int,
    max_sentences: int,
    target_words: int
):
    """Main processing function"""
    try:
        output_text, similarity = paraphrase_text(
            input_text,
            model_name,
            temperature,
            top_p,
            max_length,
            num_beams,
            max_sentences,
            target_words,
            mode
        )
        
        word_count_original = len(input_text.split())
        word_count_output = len(output_text.split())
        
        # Generate highlighted comparison
        highlighted_original, highlighted_generated, statistics = highlight_differences(
            input_text, 
            output_text
        )
        
        # Format statistics
        stats_html = format_statistics(statistics)
        
        # Basic stats line
        basic_stats = f"**Original:** {word_count_original} words | **Generated:** {word_count_output} words | **Similarity:** {similarity:.4f}"
        
        return output_text, basic_stats, similarity, highlighted_original, highlighted_generated, stats_html
    except Exception as e:
        import traceback
        error_msg = f"Error: {str(e)}\n\n{traceback.format_exc()}"
        print(error_msg)
        return error_msg, "Error occurred", 0.0, "", "", ""

# Create Gradio interface
with gr.Blocks(title="Text Paraphraser & Expander", theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # =��� Text Paraphraser & Expander
        Transform your text with AI-powered paraphrasing and expansion capabilities.
        """
    )
    
    with gr.Row():
        with gr.Column(scale=1):
            mode = gr.Radio(
                choices=["Paraphrase", "Expand"],
                value="Paraphrase",
                label="Mode",
                info="Choose to paraphrase or expand your text"
            )
            
            model_dropdown = gr.Dropdown(
                choices=list(PARAPHRASE_MODELS.keys()),
                value=list(PARAPHRASE_MODELS.keys())[0],
                label="Model Selection",
                info="Choose the model for processing"
            )
            
            gr.Markdown("### �&� Parameters")
            
            temperature = gr.Slider(
                minimum=0.0,
                maximum=2.0,
                value=0.7,
                step=0.1,
                label="Temperature",
                info="Higher = more creative, Lower = more focused"
            )
            
            top_p = gr.Slider(
                minimum=0.1,
                maximum=1.0,
                value=0.9,
                step=0.05,
                label="Top-p (Nucleus Sampling)",
                info="Probability threshold for token selection"
            )
            
            max_length = gr.Slider(
                minimum=128,
                maximum=1024,
                value=512,
                step=32,
                label="Max Length (tokens)",
                info="Maximum length of generated text per chunk"
            )
            
            num_beams = gr.Slider(
                minimum=1,
                maximum=10,
                value=4,
                step=1,
                label="Number of Beams",
                info="Higher = better quality but slower"
            )
            
            max_sentences = gr.Slider(
                minimum=1,
                maximum=10,
                value=4,
                step=1,
                label="Sentences per Chunk",
                info="Number of sentences to process together"
            )
            
            target_words = gr.Number(
                value=300,
                label="Target Word Count (Expand mode)",
                info="Approximate number of words for expansion",
                visible=False
            )
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### =��� Input Text")
            input_text = gr.Textbox(
                lines=10,
                placeholder="Enter your text here...",
                label="Original Text",
                show_copy_button=True
            )
        
        with gr.Column(scale=1):
            gr.Markdown("### =��� Generated Text")
            output_text = gr.Textbox(
                lines=10,
                label="Processed Text",
                show_copy_button=True
            )
    
    with gr.Row():
        process_btn = gr.Button("=؀� Generate", variant="primary", size="lg")
        clear_btn = gr.ClearButton([input_text, output_text], value="=���� Clear")
    
    stats_display = gr.Markdown()
    
    similarity_display = gr.Number(
        label="Cosine Similarity Score",
        precision=4,
        interactive=False
    )
    
    # Highlighted comparison section
    gr.Markdown("---")
    gr.Markdown("## =�
� Visual Comparison - See What Changed")
    
    change_stats = gr.HTML(label="Change Statistics")
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### =��� Original Text (with changes highlighted)")
            highlighted_original = gr.HTML(
                label="Original with Changes",
                show_label=False
            )
        
        with gr.Column(scale=1):
            gr.Markdown("### (' Generated Text (with changes highlighted)")
            highlighted_generated = gr.HTML(
                label="Generated with Changes",
                show_label=False
            )
    
    # Event handlers
    mode.change(
        fn=update_model_choices,
        inputs=[mode],
        outputs=[model_dropdown]
    )
    
    mode.change(
        fn=update_parameters_visibility,
        inputs=[mode],
        outputs=[target_words]
    )
    
    process_btn.click(
        fn=process_text,
        inputs=[
            input_text,
            mode,
            model_dropdown,
            temperature,
            top_p,
            max_length,
            num_beams,
            max_sentences,
            target_words
        ],
        outputs=[
            output_text, 
            stats_display, 
            similarity_display,
            highlighted_original,
            highlighted_generated,
            change_stats
        ]
    )
    
    gr.Markdown(
        """
        ---
        ### =ء� Tips:
        - **Paraphrase Mode**: Rewrites text while preserving meaning
        - **Expand Mode**: Adds details and elaboration to make text longer
        - **Sentences per Chunk**: Controls how many sentences are processed together (4 recommended)
        - Adjust temperature for creativity (0.7-1.0 for paraphrase, 1.0-1.5 for expansion)
        - Higher beam count = better quality but slower processing
        - Max length is automatically calculated based on input, but can be overridden
        - Output chunks are separated by double newlines for readability
        """
    )

if __name__ == "__main__":
    demo.launch(share=True)