# LayoutLMv3 Document Classification Demo
# Based on the CORLIDE inference pipeline

import gradio as gr
import requests
from PIL import Image
from io import BytesIO
import json
import os
import tempfile

# Mock the inference pipeline for demo purposes
class MockInferencePipeline:
    """
    Mock implementation of the inference pipeline for demo purposes.
    This simulates the behavior of the actual LayoutLMv3 inference pipeline.
    """
    
    def __init__(self):
        # Mock label names - update with your actual class labels
        self.label_names = ["Formulario A", "Formulario B", "Formulario C", "Otros"]
        
    def classify_document(self, image, text=""):
        """
        Mock classification function that simulates LayoutLMv3 inference.
        In a real implementation, this would use the actual model.
        """
        # This is a mock implementation - replace with actual model loading
        # For demo purposes, we'll return mock results
        
        import random
        confidence = random.uniform(0.7, 0.95)
        predicted_class_idx = random.randint(0, len(self.label_names) - 1)
        
        # Generate mock probabilities
        probs = [random.uniform(0.05, 0.2) for _ in range(len(self.label_names))]
        probs[predicted_class_idx] = confidence
        # Normalize to sum to 1
        total = sum(probs)
        probs = [p/total for p in probs]
        
        return {
            "predicted_class": self.label_names[predicted_class_idx],
            "confidence": confidence,
            "all_probabilities": {label: prob for label, prob in zip(self.label_names, probs)}
        }

def classify_document(image_url, text):
    """
    Classify a document using the LayoutLMv3 pipeline.
    """
    try:
        # Initialize pipeline (in real implementation, load actual model)
        pipeline = MockInferencePipeline()
        
        # Download image from URL
        response = requests.get(image_url, timeout=10)
        response.raise_for_status()
        image = Image.open(BytesIO(response.content))
        
        # Classify (this would use the actual LayoutLMv3 model in production)
        result = pipeline.classify_document(image, text)
        
        return result
        
    except Exception as e:
        return {"error": str(e)}

def format_output(result):
    """
    Format the classification result for display.
    """
    if "error" in result:
        return f"❌ Error: {result['error']}"
    
    output = f"📄 **Document Classification Results**\n\n"
    output += f"🎯 **Predicted Class:** {result['predicted_class']}\n"
    output += f"📊 **Confidence:** {result['confidence']:.4f}\n\n"
    output += "📈 **Class Probabilities:**\n"
    
    for label, prob in sorted(result['all_probabilities'].items(), key=lambda x: x[1], reverse=True):
        output += f"- {label}: {prob:.4f}\n"
    
    output += "\n💡 **Note:** This demo runs on CPU, so inference may be slower than with GPU acceleration."
    output += "\n🚧 **Status:** Version 0.1.0 - Under development (not production-ready)"
    
    return output

def create_example_inputs():
    """Create example inputs for the demo."""
    examples = [
        [
            "https://raw.githubusercontent.com/huggingface/documents/main/source/en/_static/images/readme.png",
            "Sample document text for classification"
        ],
        [
            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/question-answering.png",
            "Another sample document with different content"
        ]
    ]
    return examples

# Create Gradio interface
with gr.Blocks(title="LayoutLMv3 Document Classifier Demo", theme=gr.themes.Soft()) as demo:
    
    gr.Markdown("""
    # 📄 LayoutLMv3 Document Classification Demo
    
    This demo showcases a fine-tuned **LayoutLMv3** model for classifying legal documents (expedientes).
    The model is trained to recognize different types of Colombian government forms.
    
    ---
    
    ### 🔧 Technical Details
    - **Model:** `jmparejaz/layoutlmv3-base-expedientes`
    - **Version:** 0.1.0
    - **Framework:** Transformers + LayoutLMv3
    - **Inference:** CPU-based (slower than GPU)
    - **Status:** 🚧 Under active development
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### 📁 Input Document")
            image_input = gr.Textbox(
                label="Document Image URL",
                placeholder="Enter URL of document image (PNG/JPG)",
                lines=2
            )
            text_input = gr.Textbox(
                label="Document Text (Optional)",
                placeholder="Enter extracted text from document",
                lines=5
            )
            
            submit_btn = gr.Button("🔍 Classify Document", variant="primary", size="lg")
            
            gr.Markdown("""
            #### 📌 Example URLs:
            - `https://raw.githubusercontent.com/huggingface/documents/main/source/en/_static/images/readme.png`
            - `https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/question-answering.png`
            """)
            
        with gr.Column(scale=1):
            gr.Markdown("### 📊 Classification Results")
            output_text = gr.Textbox(
                label="Results",
                lines=15,
                interactive=False
            )
    
    # Examples
    gr.Examples(
        examples=create_example_inputs(),
        inputs=[image_input, text_input],
        outputs=output_text,
        fn=classify_document,
        cache_examples=True,
        label="Try these examples:"
    )
    
    # Main classification flow
    submit_btn.click(
        fn=classify_document,
        inputs=[image_input, text_input],
        outputs=output_text
    ).then(
        fn=format_output,
        inputs=output_text,
        outputs=output_text
    )
    
    gr.Markdown("""
    ---
    
    ### 📚 About This Model
    
    This LayoutLMv3 model has been fine-tuned on Colombian legal documents (expedientes) to classify different form types.
    
    **Key Features:**
    - Multimodal: Combines visual layout and text information
    - Specialized: Trained specifically for legal document classification
    - Efficient: Optimized for document understanding tasks
    
    **Limitations:**
    - ⚠️ **Version 0.1.0**: This is an early development version
    - ⚠️ **CPU Inference**: Demo runs on CPU for accessibility (slower than GPU)
    - ⚠️ **Not Production-Ready**: Model is still undergoing improvements
    
    **Model Card:** [jmparejaz/layoutlmv3-base-expedientes](https://huggingface.co/jmparejaz/layoutlmv3-base-expedientes)
    
    ---
    
    ### 🛠️ Implementation Notes
    
    This demo uses a mock inference pipeline for demonstration purposes. In production, you would:
    
    1. Load the actual LayoutLMv3 model from Hugging Face
    2. Use the LayoutLMv3Processor for proper image/text processing
    3. Run inference on GPU for better performance
    4. Handle batch processing for multiple documents
    
    **Production Code Example:**
    ```python
    from transformers import AutoModelForSequenceClassification, LayoutLMv3Processor
    
    model = AutoModelForSequenceClassification.from_pretrained("jmparejaz/layoutlmv3-base-expedientes")
    processor = LayoutLMv3Processor.from_pretrained("jmparejaz/layoutlmv3-base-expedientes")
    
    # Process document
    inputs = processor(
        images=image,
        text=text,
        return_tensors="pt",
        padding=True,
        truncation=True
    )
    
    outputs = model(**inputs)
    predictions = outputs.logits.argmax(dim=-1)
    ```
    """)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)