import gradio as gr
from transformers import pipeline
import re

# Custom sentence tokenizer
def sent_tokenize(text):
    sentence_endings = re.compile(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?|!)(\s|$)')
    sentences = sentence_endings.split(text)
    return [s.strip() for s in sentences if s.strip()]

# Initialize the classifiers
zero_shot_classifier = pipeline("zero-shot-classification", model="tasksource/ModernBERT-base-nli", device="cpu")
nli_classifier = pipeline("text-classification", model="tasksource/ModernBERT-base-nli", device="cpu")

# [Previous example definitions remain the same]
zero_shot_examples = [
    ["I absolutely love this product, it's amazing!", "positive, negative, neutral"],
    ["I need to buy groceries", "shopping, urgent tasks, leisure, philosophy"],
    ["The sun is very bright today", "weather, astronomy, complaints, poetry"],
    ["I love playing video games", "entertainment, sports, education, business"],
    ["The car won't start", "transportation, art, cooking, literature"]
]

nli_examples = [
    ["A man is sleeping on a couch", "The man is awake"],
    ["The restaurant's waiting area is bustling, but several tables remain vacant", "The establishment is at maximum capacity"],
    ["The child is methodically arranging blocks while frowning in concentration", "The kid is experiencing joy"],
    ["Dark clouds are gathering and the pavement shows scattered wet spots", "It's been raining heavily all day"],
    ["A German Shepherd is exhibiting defensive behavior towards someone approaching the property", "The animal making noise is feline"]
]

long_context_examples = [
    ["""A company's environmental policy typically has a profound impact upon its standing in the community. There are legal regulations, with stiff penalties attached, which compel managers to ensure that any waste products are disposed of without contaminating the air or water supplies. In addition, employees can be educated about the inevitable commercial and social benefits of recycling paper and other substances produced as by-products of the manufacturing process. One popular method for gaining staff co-operation is the internal incentive scheme. These often target teams rather than individuals, since the interdependence of staff organising any reprocessing, masks the importance of a given player's role.""",
     "The regard held for an organisation may be affected by its commitment to environmental issues."]
]

def get_label_color(label, confidence=1.0):
    """Return color based on NLI label with confidence-based saturation."""
    base_colors = {
        'entailment': 'rgb(144, 238, 144)',    # Light green
        'neutral': 'rgb(255, 229, 180)',       # Peach
        'contradiction': 'rgb(255, 182, 193)'   # Light pink
    }
    
    # Convert RGB color to RGBA with confidence-based alpha
    if label in base_colors:
        rgb = base_colors[label].replace('rgb(', '').replace(')', '').split(',')
        r, g, b = map(int, rgb)
        # Adjust the color based on confidence
        alpha = 0.3 + (0.7 * confidence)  # Range from 0.3 to 1.0
        return f"rgba({r},{g},{b},{alpha})"
    return '#FFFFFF'

def create_analysis_html(sentence_results, global_label, global_confidence):
    """Create HTML table for sentence analysis with color coding and confidence."""
    html = """
    <style>
        .analysis-table {
            width: 100%;
            border-collapse: collapse;
            margin: 20px 0;
            font-family: Arial, sans-serif;
        }
        .analysis-table th, .analysis-table td {
            padding: 12px;
            border: 1px solid #ddd;
            text-align: left;
        }
        .analysis-table th {
            background-color: #f5f5f5;
        }
        .global-prediction {
            padding: 15px;
            margin: 20px 0;
            border-radius: 5px;
            font-weight: bold;
        }
        .confidence {
            font-size: 0.9em;
            color: #666;
        }
    </style>
    """
    
    # Add global prediction box with confidence
    html += f"""
    <div class="global-prediction" style="background-color: {get_label_color(global_label, global_confidence)}">
        Global Prediction: {global_label} 
        <span class="confidence">(Confidence: {global_confidence:.2%})</span>
    </div>
    """
    
    # Create table
    html += """
    <table class="analysis-table">
        <tr>
            <th>Sentence</th>
            <th>Prediction</th>
            <th>Confidence</th>
        </tr>
    """
    
    # Add rows for each sentence
    for result in sentence_results:
        html += f"""
        <tr style="background-color: {get_label_color(result['prediction'], result['confidence'])}">
            <td>{result['sentence']}</td>
            <td>{result['prediction']}</td>
            <td>{result['confidence']:.2%}</td>
        </tr>
        """
    
    html += "</table>"
    return html


def process_input(text_input, labels_or_premise, mode):
    if mode == "Zero-Shot Classification":
        labels = [label.strip() for label in labels_or_premise.split(',')]
        prediction = zero_shot_classifier(text_input, labels)
        results = {label: score for label, score in zip(prediction['labels'], prediction['scores'])}
        return results, ''
    elif mode == "Natural Language Inference":
        pred = nli_classifier([{"text": text_input, "text_pair": labels_or_premise}], return_all_scores=True)[0]
        results = {pred['label']: pred['score'] for pred in pred}
        return results, ''
    else:  # Long Context NLI
        # Global prediction
        global_pred = nli_classifier([{"text": text_input, "text_pair": labels_or_premise}], return_all_scores=True)[0]
        global_results = {p['label']: p['score'] for p in global_pred}
        global_label = max(global_results.items(), key=lambda x: x[1])[0]
        global_confidence = max(global_results.values())
        
        # Sentence-level analysis
        sentences = sent_tokenize(text_input)
        sentence_results = []
        
        for sentence in sentences:
            sent_pred = nli_classifier([{"text": sentence, "text_pair": labels_or_premise}], return_all_scores=True)[0]
            # Get the prediction and confidence for the sentence
            pred_scores = [(p['label'], p['score']) for p in sent_pred]
            max_pred = max(pred_scores, key=lambda x: x[1])
            max_label, confidence = max_pred
            
            sentence_results.append({
                'sentence': sentence,
                'prediction': max_label,
                'confidence': confidence
            })
        
        analysis_html = create_analysis_html(sentence_results, global_label, global_confidence)
        return global_results, analysis_html

def update_interface(mode):
    if mode == "Zero-Shot Classification":
        return (
            gr.update(
                label="🏷️ Categories", 
                placeholder="Enter comma-separated categories...",
                value=zero_shot_examples[0][1]
            ),
            gr.update(value=zero_shot_examples[0][0])
        )
    elif mode == "Natural Language Inference":
        return (
            gr.update(
                label="🔎 Hypothesis", 
                placeholder="Enter a hypothesis to compare with the premise...",
                value=nli_examples[0][1]
            ),
            gr.update(value=nli_examples[0][0])
        )
    else:  # Long Context NLI
        return (
            gr.update(
                label="🔎 Hypothesis",
                placeholder="Enter a hypothesis to test against the full context...",
                value=long_context_examples[0][1]
            ),
            gr.update(value=long_context_examples[0][0])
        )

def update_visibility(mode):
    return (
        gr.update(visible=(mode == "Zero-Shot Classification")),
        gr.update(visible=(mode == "Natural Language Inference")),
        gr.update(visible=(mode == "Long Context NLI"))
    )

# Now define the Blocks interface
with gr.Blocks() as demo:
    gr.Markdown("""
    # tasksource/ModernBERT-nli demonstration
    
    This space uses [tasksource/ModernBERT-base-nli](https://huggingface.co/tasksource/ModernBERT-base-nli), 
    fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) 
    on tasksource classification tasks. 
    This NLI model achieves high accuracy on categorization, logical reasoning and long-context NLI, outperforming Llama 3 8B on ConTRoL (long-context NLI) and FOLIO (logical reasoning).
    """)

    mode = gr.Radio(
        ["Zero-Shot Classification", "Natural Language Inference", "Long Context NLI"],
        label="Select Mode",
        value="Zero-Shot Classification"
    )
    
    with gr.Column():
        text_input = gr.Textbox(
            label="✍️ Input Text",
            placeholder="Enter your text...",
            lines=3,
            value=zero_shot_examples[0][0]
        )
        
        labels_or_premise = gr.Textbox(
            label="🏷️ Categories",
            placeholder="Enter comma-separated categories...",
            lines=2,
            value=zero_shot_examples[0][1]
        )
        
        submit_btn = gr.Button("Submit")
        
        outputs = [
            gr.Label(label="📊 Results"),
            gr.HTML(label="📈 Sentence Analysis")
        ]

        with gr.Column(variant="panel") as zero_shot_examples_panel:
            gr.Examples(
                examples=zero_shot_examples,
                inputs=[text_input, labels_or_premise],
                label="Zero-Shot Classification Examples",
            )
    
        with gr.Column(variant="panel") as nli_examples_panel:
            gr.Examples(
                examples=nli_examples,
                inputs=[text_input, labels_or_premise],
                label="Natural Language Inference Examples",
            )
            
        with gr.Column(variant="panel") as long_context_examples_panel:
            gr.Examples(
                examples=long_context_examples,
                inputs=[text_input, labels_or_premise],
                label="Long Context NLI Examples",
            )

    mode.change(
        fn=update_interface,
        inputs=[mode],
        outputs=[labels_or_premise, text_input]
    )
    
    mode.change(
        fn=update_visibility,
        inputs=[mode],
        outputs=[zero_shot_examples_panel, nli_examples_panel, long_context_examples_panel]
    )
    
    submit_btn.click(
        fn=process_input,
        inputs=[text_input, labels_or_premise, mode],
        outputs=outputs
    )

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