modules/educational_module.py

import streamlit as st
import io
import tempfile
from pathlib import Path
from datetime import datetime
from layout import tool_container, key_concept, research_question, upload_container
import sys

# Import the necessary modules for OCR processing
sys.path.append(str(Path(__file__).parent.parent))
try:
    from process_file import process_file as process_file_util
    process_file = process_file_util
except ImportError:
    # Fallback if process_file is not available
    def process_file(uploaded_file, use_vision=True, custom_prompt=None):
        """Fallback function for processing files"""
        st.warning("Using mock processing function. Real OCR functionality is not available.")
        return {
            "file_name": uploaded_file.name,
            "languages": ["English"],
            "topics": ["History", "Document"],
            "ocr_contents": {
                "content": f"This is mock OCR content for {uploaded_file.name}. Vision model: {use_vision}"
            }
        }

def render():
    """Module 5: Interactive OCR Experiment"""
    
    st.title("Module 5: Interactive OCR Experiment")
    
    # Introduction to the interactive experiment
    intro_content = """
    <h3>Interactive OCR Experiment</h3>
    <p>
    This interactive experiment allows you to process historical documents with OCR and analyze the results.
    Try different settings and compare the outcomes to understand the strengths and limitations of OCR technology.
    </p>
    """
    st.markdown(intro_content, unsafe_allow_html=True)
    
    # Create tabs for different activities
    experiment_tab, compare_tab, analyze_tab = st.tabs(["Process Documents", "Compare Results", "Analysis Guide"])
    
    # Try to import PDF tools if available
    try:
        from pdf2image import convert_from_bytes
        pdf_support = True
    except ImportError:
        pdf_support = False
        st.warning("PDF preview functionality is limited. The pdf2image module is required for PDF previews.")
    
    with experiment_tab:
        # Create a two-column layout
        col1, col2 = st.columns([1, 1])
        
        with col1:
            # Tool container for document selection and options
            st.subheader("Step 1: Select Document & Options")
            
            # Processing options
            use_vision = st.checkbox("Use Vision Model", value=True,
                                 help="Use the vision model for improved analysis")
            
            # Additional prompt
            st.markdown("### Custom Research Prompt (Optional)")
            st.markdown("""Provide additional instructions to guide the OCR analysis. 
            Focus on specific aspects of historical research you're interested in.""")
            custom_prompt = st.text_area("Research Prompt",
                                       placeholder="E.g., Focus on identifying dates and historical figures...",
                                       help="Optional instructions to guide the analysis")
            
            # Sample document selection
            input_dir = Path(__file__).parent.parent / "input"
            
            if input_dir.exists():
                sample_files = list(input_dir.glob("*.jpg")) + list(input_dir.glob("*.png")) + list(input_dir.glob("*.pdf"))
                
                if sample_files:
                    st.markdown("#### Sample Documents")
                    sample_options = ["Upload my own document"] + [f.name for f in sample_files]
                    sample_choice = st.selectbox("Choose a document:", sample_options)
                    
                    if sample_choice != "Upload my own document":
                        # Process the selected sample file
                        selected_file = next((f for f in sample_files if f.name == sample_choice), None)
                        
                        if selected_file:
                            # Store the selected sample file in session state
                            with open(selected_file, "rb") as f:
                                file_bytes = f.read()
                            
                            st.session_state.sample_file = {
                                "name": selected_file.name,
                                "bytes": file_bytes
                            }
                            
                            # Preview the selected sample
                            if selected_file.suffix.lower() == ".pdf" and pdf_support:
                                try:
                                    with st.spinner("Generating PDF preview..."):
                                        images = convert_from_bytes(file_bytes, first_page=1, last_page=1, dpi=150)
                                        if images:
                                            st.image(images[0], caption=f"Preview: {selected_file.name}", use_column_width=True)
                                except Exception:
                                    st.info(f"PDF selected: {selected_file.name}")
                            else:
                                # For images display directly
                                try:
                                    from PIL import Image
                                    img = Image.open(io.BytesIO(file_bytes))
                                    st.image(img, caption=f"Preview: {selected_file.name}", use_column_width=True)
                                except Exception:
                                    st.info(f"Selected: {selected_file.name}")
                    else:
                        # Clear the sample file if "Upload my own" is selected
                        if 'sample_file' in st.session_state:
                            del st.session_state.sample_file
                        
                        # Display file uploader
                        upload_html = """
                        <h4>Upload a document to get started</h4>
                        <p>Supported formats: PDF, JPG, PNG</p>
                        """
                        
                        upload_container(upload_html)
                        uploaded_file = st.file_uploader("Choose a file", type=["pdf", "png", "jpg", "jpeg"], label_visibility="collapsed")
                        
                        if uploaded_file is not None:
                            # Display preview of the uploaded file
                            file_ext = Path(uploaded_file.name).suffix.lower()
                            
                            if file_ext == ".pdf" and pdf_support:
                                try:
                                    # Convert first page of PDF to image for preview
                                    pdf_bytes = uploaded_file.getvalue()
                                    with st.spinner("Generating PDF preview..."):
                                        images = convert_from_bytes(pdf_bytes, first_page=1, last_page=1, dpi=150)
                                        if images:
                                            st.image(images[0], caption=f"PDF Preview: {uploaded_file.name}", use_column_width=True)
                                        else:
                                            st.info(f"PDF uploaded: {uploaded_file.name}")
                                except Exception:
                                    st.info(f"PDF uploaded: {uploaded_file.name}")
                            elif file_ext != ".pdf":
                                st.image(uploaded_file, use_column_width=True)
                            else:
                                st.info(f"PDF uploaded: {uploaded_file.name}")
                else:
                    # No sample files, just show the uploader
                    upload_html = """
                    <h4>Upload a document to get started</h4>
                    <p>Supported formats: PDF, JPG, PNG</p>
                    """
                    
                    upload_container(upload_html)
                    uploaded_file = st.file_uploader("Choose a file", type=["pdf", "png", "jpg", "jpeg"], label_visibility="collapsed")
                    
                    if uploaded_file is not None:
                        # Display the file preview
                        file_ext = Path(uploaded_file.name).suffix.lower()
                        if file_ext == ".pdf" and pdf_support:
                            try:
                                pdf_bytes = uploaded_file.getvalue()
                                with st.spinner("Generating PDF preview..."):
                                    images = convert_from_bytes(pdf_bytes, first_page=1, last_page=1, dpi=150)
                                    if images:
                                        st.image(images[0], caption=f"PDF Preview: {uploaded_file.name}", use_column_width=True)
                            except Exception:
                                st.info(f"PDF uploaded: {uploaded_file.name}")
                        elif file_ext != ".pdf":
                            st.image(uploaded_file, use_column_width=True)
                        else:
                            st.info(f"PDF uploaded: {uploaded_file.name}")
            else:
                # No input directory
                upload_html = """
                <h4>Upload a document to get started</h4>
                <p>Supported formats: PDF, JPG, PNG</p>
                """
                
                upload_container(upload_html)
                uploaded_file = st.file_uploader("Choose a file", type=["pdf", "png", "jpg", "jpeg"], label_visibility="collapsed")
            
            # Process button
            st.subheader("Step 2: Process the Document")
            
            # Get the file to process (either uploaded or sample)
            file_to_process = None
            if 'sample_file' in st.session_state and sample_choice != "Upload my own document":
                # Create a FileUploader-like object from the sample file
                class SampleFileObject:
                    def __init__(self, name, data):
                        self.name = name
                        self._data = data
                    
                    def getvalue(self):
                        return self._data
                
                file_to_process = SampleFileObject(
                    st.session_state.sample_file["name"],
                    st.session_state.sample_file["bytes"]
                )
            elif 'uploaded_file' in locals() and uploaded_file is not None:
                file_to_process = uploaded_file
            
            # Process button
            process_button = st.button(
                "Process Document",
                disabled=file_to_process is None,
                use_container_width=True
            )
            
            if process_button and file_to_process is not None:
                with st.spinner("Processing document..."):
                    try:
                        # Process the file
                        result = process_file(file_to_process, use_vision, custom_prompt=custom_prompt if custom_prompt else None)
                        
                        if result:
                            st.success("Document processed successfully!")
                            
                            # Store result in session state for display in the right column
                            st.session_state.current_result = result
                            
                            # Add to processing history
                            history_item = {
                                "id": datetime.now().timestamp(),
                                "fileName": file_to_process.name,
                                "timestamp": datetime.now().isoformat(),
                                "result": result,
                                "useVision": use_vision
                            }
                            
                            if 'processing_history' not in st.session_state:
                                st.session_state.processing_history = []
                            
                            st.session_state.processing_history.append(history_item)
                            
                            st.experimental_rerun()
                        else:
                            st.error("Failed to process document.")
                    except Exception as e:
                        st.error(f"Error processing document: {str(e)}")
            
            # Experiment instructions
            experiment_content = """
            <h3>Experiment Instructions</h3>
            <ol>
                <li><strong>Step 1:</strong> Select a document and choose your options</li>
                <li><strong>Step 2:</strong> Process the document with the selected options</li>
                <li><strong>Step 3:</strong> Analyze the results in the panel on the right</li>
                <li><strong>Step 4:</strong> Try again with different settings (e.g., toggle vision model)</li>
                <li><strong>Step 5:</strong> Compare results between different runs</li>
            </ol>
            """
            key_concept(experiment_content)
        
        with col2:
            # Results display
            st.subheader("Step 3: View Results")
            
            if 'current_result' in st.session_state and st.session_state.current_result:
                result = st.session_state.current_result
                
                # Display results in a tool container
                result_html = f"""
                <h4>Results for: {result.get('file_name', 'Unknown')}</h4>
                <p><strong>Languages:</strong> {', '.join(result.get('languages', ['Unknown']))}</p>
                <p><strong>Topics:</strong> {', '.join(result.get('topics', ['Unknown']))}</p>
                """
                tool_container(result_html)
                
                # Create tabs for different views
                tab1, tab2 = st.tabs(["Structured View", "Raw JSON"])
                
                with tab1:
                    # Display in a more user-friendly format
                    if 'ocr_contents' in result:
                        if isinstance(result['ocr_contents'], dict):
                            for section, content in result['ocr_contents'].items():
                                if content:  # Only display non-empty sections
                                    st.markdown(f"#### {section.replace('_', ' ').title()}")
                                    
                                    if isinstance(content, str):
                                        st.markdown(content)
                                    elif isinstance(content, list):
                                        for item in content:
                                            if isinstance(item, str):
                                                st.markdown(f"- {item}")
                                            elif isinstance(item, dict):
                                                st.json(item)
                                    elif isinstance(content, dict):
                                        for k, v in content.items():
                                            st.markdown(f"**{k}:** {v}")
                
                with tab2:
                    # Show the raw JSON
                    st.json(result)
                
                # Download options
                st.markdown("### Export Results")
                
                col1, col2 = st.columns(2)
                
                with col1:
                    # Export as JSON
                    import json
                    json_bytes = json.dumps(result, indent=2).encode()
                    st.download_button(
                        label="Download JSON",
                        data=json_bytes,
                        file_name="ocr_results.json",
                        mime="application/json",
                        use_container_width=True
                    )
                
                with col2:
                    # Export as text if content is available
                    if 'ocr_contents' in result and isinstance(result['ocr_contents'], dict) and 'content' in result['ocr_contents']:
                        text_content = result['ocr_contents']['content']
                        st.download_button(
                            label="Download Text",
                            data=text_content.encode(),
                            file_name="ocr_text.txt",
                            mime="text/plain",
                            use_container_width=True
                        )
            else:
                # Show placeholder when no results are available
                placeholder_html = """
                <h4>Results will appear here</h4>
                <p>Upload and process a document to see the OCR results in this panel.</p>
                <p>The OCR tool will:</p>
                <ol>
                    <li>Extract text from your document</li>
                    <li>Identify languages and topics</li>
                    <li>Provide structured content analysis</li>
                    <li>Generate downloadable results</li>
                </ol>
                """
                tool_container(placeholder_html)
            
            # Display processing history if available
            if 'processing_history' in st.session_state and st.session_state.processing_history:
                st.subheader("Step 4: Review Processing History")
                
                # Most recent result
                latest = st.session_state.processing_history[-1]
                latest_html = f"""
                <h4>Latest Document: {latest['fileName']}</h4>
                <p><strong>Processed at:</strong> {datetime.fromisoformat(latest['timestamp']).strftime('%Y-%m-%d %H:%M')}</p>
                <p><strong>Vision model used:</strong> {'Yes' if latest['useVision'] else 'No'}</p>
                """
                tool_container(latest_html)
                
                # History in expander
                with st.expander("View Complete Processing History"):
                    for i, item in enumerate(reversed(st.session_state.processing_history)):
                        st.markdown(f"""
                        <div style="background-color: var(--color-gray-700); padding: 0.75rem; border-radius: 0.5rem; margin-bottom: 0.5rem;">
                        <strong>{item['fileName']}</strong><br>
                        {datetime.fromisoformat(item['timestamp']).strftime('%Y-%m-%d %H:%M')} - 
                        Vision model: {'Yes' if item['useVision'] else 'No'}
                        </div>
                        """, unsafe_allow_html=True)
                        
                        # Option to view a previous result
                        if st.button(f"View This Result", key=f"view_history_{i}"):
                            st.session_state.current_result = item['result']
                            st.experimental_rerun()
    
    # Compare tab for side-by-side comparison
    with compare_tab:
        st.subheader("Compare OCR Results")
        
        if 'processing_history' in st.session_state and len(st.session_state.processing_history) >= 2:
            st.markdown("""
            Select two processing results to compare side by side. This allows you to see
            how different options (like using the vision model) affect OCR quality.
            """)
            
            # Create selection dropdowns for the documents
            col1, col2 = st.columns(2)
            with col1:
                # First document selector
                doc_options_1 = [f"{i+1}: {item['fileName']} ({'Vision' if item['useVision'] else 'No Vision'})" 
                               for i, item in enumerate(st.session_state.processing_history)]
                doc_choice_1 = st.selectbox("First Document:", doc_options_1, key="compare_doc_1")
                doc_index_1 = int(doc_choice_1.split(":")[0]) - 1
                
            with col2:
                # Second document selector
                doc_options_2 = [f"{i+1}: {item['fileName']} ({'Vision' if item['useVision'] else 'No Vision'})" 
                               for i, item in enumerate(st.session_state.processing_history)]
                default_index = min(1, len(st.session_state.processing_history) - 1)  # Default to second item
                doc_choice_2 = st.selectbox("Second Document:", doc_options_2, key="compare_doc_2", index=default_index)
                doc_index_2 = int(doc_choice_2.split(":")[0]) - 1
            
            # Retrieve the selected documents
            doc1 = st.session_state.processing_history[doc_index_1]
            doc2 = st.session_state.processing_history[doc_index_2]
            
            # Show comparison
            col1, col2 = st.columns(2)
            
            with col1:
                doc1_html = f"""
                <h4>Document 1: {doc1['fileName']}</h4>
                <p><strong>Processed at:</strong> {datetime.fromisoformat(doc1['timestamp']).strftime('%Y-%m-%d %H:%M')}</p>
                <p><strong>Vision model used:</strong> {'Yes' if doc1['useVision'] else 'No'}</p>
                """
                tool_container(doc1_html)
                
                # Display content summary
                if 'ocr_contents' in doc1['result'] and isinstance(doc1['result']['ocr_contents'], dict):
                    if 'content' in doc1['result']['ocr_contents']:
                        content = doc1['result']['ocr_contents']['content']
                        st.markdown(f"""
                        <div style="max-height: 300px; overflow-y: auto; word-wrap: break-word; 
                                   border: 1px solid #374151; padding: 1rem; background-color: #1f2937;">
                            {content[:500]}{'...' if len(content) > 500 else ''}
                        </div>
                        """, unsafe_allow_html=True)
            
            with col2:
                doc2_html = f"""
                <h4>Document 2: {doc2['fileName']}</h4>
                <p><strong>Processed at:</strong> {datetime.fromisoformat(doc2['timestamp']).strftime('%Y-%m-%d %H:%M')}</p>
                <p><strong>Vision model used:</strong> {'Yes' if doc2['useVision'] else 'No'}</p>
                """
                tool_container(doc2_html)
                
                # Display content summary
                if 'ocr_contents' in doc2['result'] and isinstance(doc2['result']['ocr_contents'], dict):
                    if 'content' in doc2['result']['ocr_contents']:
                        content = doc2['result']['ocr_contents']['content']
                        st.markdown(f"""
                        <div style="max-height: 300px; overflow-y: auto; word-wrap: break-word; 
                                   border: 1px solid #374151; padding: 1rem; background-color: #1f2937;">
                            {content[:500]}{'...' if len(content) > 500 else ''}
                        </div>
                        """, unsafe_allow_html=True)
            
            # Comparison analysis
            if doc1['fileName'] == doc2['fileName'] and doc1['useVision'] != doc2['useVision']:
                comparison_content = """
                <h3>Vision vs. Non-Vision Model Comparison</h3>
                <p>You're comparing the same document processed with different models. 
                This is an excellent way to evaluate the impact of vision capabilities on OCR accuracy.</p>
                
                <p>Look for these differences:</p>
                <ul>
                  <li>Completeness of extracted text</li>
                  <li>Accuracy of layout understanding</li>
                  <li>Recognition of complex elements (tables, figures)</li>
                  <li>Topic and language detection accuracy</li>
                </ul>
                """
                key_concept(comparison_content)
        else:
            need_more_content = """
            <h3>Need More Documents to Compare</h3>
            <p>Process at least two documents to enable side-by-side comparison. Try processing 
            the same document with and without the vision model to see the differences in OCR quality.</p>
            """
            research_question(need_more_content)
    
    # Analysis guide tab
    with analyze_tab:
        st.subheader("Analysis Guide")
        
        st.markdown("""
        ### How to Analyze OCR Results
        
        When analyzing OCR results from historical documents, consider these key factors:
        
        1. **Text Accuracy**
           - Check for common OCR errors (e.g., mistaking "e" for "c", "l" for "1")
           - Assess recognition of period-specific typography and writing styles
           - Evaluate handling of degraded or damaged text areas
        
        2. **Structure Preservation**
           - Does the OCR maintain paragraph and section breaks?
           - Are columns and tabular data correctly preserved?
           - How well are page transitions handled?
        
        3. **Special Elements**
           - Recognition of footnotes, marginalia, and annotations
           - Handling of illustrations, diagrams, and decorative elements
           - Treatment of watermarks, signatures, and stamps
        
        4. **Metadata Extraction**
           - Accuracy of detected languages, topics, and document type
           - Identification of dates, names, and key entities
           - Recognition of document purpose and context
        """)
        
        col1, col2 = st.columns(2)
        
        with col1:
            challenge_content = """
            <h3>Common OCR Challenges</h3>
            <ul>
                <li><strong>Typography Variations</strong>: Historical fonts that differ from modern text</li>
                <li><strong>Material Degradation</strong>: Fading, stains, tears affecting legibility</li>
                <li><strong>Handwritten Elements</strong>: Marginalia, signatures, and annotations</li>
                <li><strong>Complex Layouts</strong>: Multi-column formats and decorative elements</li>
                <li><strong>Language and Terminology</strong>: Archaic terms and multilingual content</li>
            </ul>
            """
            gray_container(challenge_content)
        
        with col2:
            tips_content = """
            <h3>Making the Most of OCR Results</h3>
            <ul>
                <li><strong>Contextual Reading</strong>: Use context to interpret unclear passages</li>
                <li><strong>Error Patterns</strong>: Identify and correct systematic OCR errors</li>
                <li><strong>Hybrid Analysis</strong>: Combine OCR search with close reading</li>
                <li><strong>Comparative Processing</strong>: Try different settings on documents</li>
                <li><strong>Iterative Refinement</strong>: Use insights to improve future processing</li>
            </ul>
            """
            gray_container(tips_content)
        
        # Show example analysis if there's processing history
        if 'processing_history' in st.session_state and st.session_state.processing_history:
            with st.expander("Example Analysis from Your Documents"):
                # Pick the latest document
                latest = st.session_state.processing_history[-1]
                
                st.markdown(f"""
                #### Sample Analysis for: {latest['fileName']}
                
                **Document Context:**
                - Languages: {', '.join(latest['result'].get('languages', ['Unknown']))}
                - Topics: {', '.join(latest['result'].get('topics', ['Unknown']))}
                - Vision model used: {'Yes' if latest['useVision'] else 'No'}
                
                **What to Look For:**
                1. Check how well the model identified key topics and languages
                2. Evaluate the completeness of extracted text
                3. Note any systematic errors in text recognition
                4. Assess how well document structure was preserved
                """)