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| import streamlit as st | |
| from PIL import Image | |
| import torch | |
| import json | |
| from transformers import ( | |
| DonutProcessor, | |
| VisionEncoderDecoderModel, | |
| LayoutLMv3Processor, | |
| LayoutLMv3ForSequenceClassification, | |
| BrosProcessor, | |
| BrosForTokenClassification, | |
| LlavaProcessor, | |
| LlavaForConditionalGeneration | |
| ) | |
| # Cache the model loading to improve performance | |
| def load_model(model_name): | |
| """Load the selected model and processor""" | |
| try: | |
| if model_name == "Donut": | |
| processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base") | |
| model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base") | |
| # Configure Donut specific parameters | |
| model.config.decoder_start_token_id = processor.tokenizer.bos_token_id | |
| model.config.pad_token_id = processor.tokenizer.pad_token_id | |
| model.config.vocab_size = len(processor.tokenizer) | |
| elif model_name == "LayoutLMv3": | |
| processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base") | |
| model = LayoutLMv3ForSequenceClassification.from_pretrained("microsoft/layoutlmv3-base") | |
| elif model_name == "BROS": | |
| processor = BrosProcessor.from_pretrained("microsoft/bros-base") | |
| model = BrosForTokenClassification.from_pretrained("microsoft/bros-base") | |
| elif model_name == "LLaVA-1.5": | |
| processor = LlavaProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf") | |
| model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf") | |
| return model, processor | |
| except Exception as e: | |
| st.error(f"Error loading model {model_name}: {str(e)}") | |
| return None, None | |
| def analyze_document(image, model_name, model, processor): | |
| """Analyze document using selected model""" | |
| try: | |
| # Process image according to model requirements | |
| if model_name == "Donut": | |
| # Prepare input with task prompt | |
| pixel_values = processor(image, return_tensors="pt").pixel_values | |
| task_prompt = "<s_cord>analyze the document and extract information</s_cord>" | |
| decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids | |
| # Generate output with improved parameters | |
| outputs = model.generate( | |
| pixel_values, | |
| decoder_input_ids=decoder_input_ids, | |
| max_length=512, | |
| early_stopping=True, | |
| pad_token_id=processor.tokenizer.pad_token_id, | |
| eos_token_id=processor.tokenizer.eos_token_id, | |
| use_cache=True, | |
| num_beams=4, | |
| bad_words_ids=[[processor.tokenizer.unk_token_id]], | |
| return_dict_in_generate=True | |
| ) | |
| # Process and clean the output | |
| sequence = processor.batch_decode(outputs.sequences)[0] | |
| sequence = sequence.replace(task_prompt, "").replace("</s_cord>", "").strip() | |
| # Try to parse as JSON, fallback to raw text | |
| try: | |
| result = json.loads(sequence) | |
| except json.JSONDecodeError: | |
| result = {"raw_text": sequence} | |
| elif model_name == "LayoutLMv3": | |
| inputs = processor(image, return_tensors="pt") | |
| outputs = model(**inputs) | |
| result = {"logits": outputs.logits.tolist()} # Convert tensor to list for JSON serialization | |
| elif model_name == "BROS": | |
| inputs = processor(image, return_tensors="pt") | |
| outputs = model(**inputs) | |
| result = {"predictions": outputs.logits.tolist()} | |
| elif model_name == "LLaVA-1.5": | |
| inputs = processor(image, return_tensors="pt") | |
| outputs = model.generate(**inputs, max_length=256) | |
| result = {"generated_text": processor.decode(outputs[0], skip_special_tokens=True)} | |
| return result | |
| except Exception as e: | |
| error_msg = str(e) | |
| st.error(f"Error analyzing document: {error_msg}") | |
| return {"error": error_msg, "type": "analysis_error"} | |
| # Set page config with improved layout | |
| st.set_page_config( | |
| page_title="Document Analysis Comparison", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # Add custom CSS for better styling | |
| st.markdown(""" | |
| <style> | |
| .stAlert { | |
| margin-top: 1rem; | |
| } | |
| .upload-text { | |
| font-size: 1.2rem; | |
| margin-bottom: 1rem; | |
| } | |
| .model-info { | |
| padding: 1rem; | |
| border-radius: 0.5rem; | |
| background-color: #f8f9fa; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Title and description | |
| st.title("Document Understanding Model Comparison") | |
| st.markdown(""" | |
| Compare different models for document analysis and understanding. | |
| Upload an image and select a model to analyze it. | |
| """) | |
| # Create two columns for layout | |
| col1, col2 = st.columns([1, 1]) | |
| with col1: | |
| # File uploader with improved error handling | |
| uploaded_file = st.file_uploader( | |
| "Choose a document image", | |
| type=['png', 'jpg', 'jpeg', 'pdf'], | |
| help="Supported formats: PNG, JPEG, PDF" | |
| ) | |
| if uploaded_file is not None: | |
| try: | |
| # Display uploaded image | |
| image = Image.open(uploaded_file) | |
| st.image(image, caption='Uploaded Document', use_column_width=True) | |
| except Exception as e: | |
| st.error(f"Error loading image: {str(e)}") | |
| with col2: | |
| # Model selection with detailed information | |
| model_info = { | |
| "Donut": { | |
| "description": "Best for structured OCR and document format understanding", | |
| "memory": "6-8GB", | |
| "strengths": ["Structured OCR", "Memory efficient", "Good with fixed formats"], | |
| "best_for": ["Invoices", "Forms", "Structured documents"] | |
| }, | |
| "LayoutLMv3": { | |
| "description": "Strong layout understanding with reasoning capabilities", | |
| "memory": "12-15GB", | |
| "strengths": ["Layout understanding", "Reasoning", "Pre-trained knowledge"], | |
| "best_for": ["Complex layouts", "Mixed content", "Tables"] | |
| }, | |
| "BROS": { | |
| "description": "Memory efficient with fast inference", | |
| "memory": "4-6GB", | |
| "strengths": ["Fast inference", "Memory efficient", "Easy fine-tuning"], | |
| "best_for": ["Simple documents", "Quick analysis", "Basic OCR"] | |
| }, | |
| "LLaVA-1.5": { | |
| "description": "Comprehensive OCR with strong reasoning", | |
| "memory": "25-40GB", | |
| "strengths": ["Strong reasoning", "Zero-shot capable", "Visual understanding"], | |
| "best_for": ["Complex documents", "Natural language understanding", "Visual QA"] | |
| } | |
| } | |
| selected_model = st.selectbox( | |
| "Select Model", | |
| list(model_info.keys()) | |
| ) | |
| # Display enhanced model information | |
| st.markdown("### Model Details") | |
| with st.expander("Model Information", expanded=True): | |
| st.markdown(f"**Description:** {model_info[selected_model]['description']}") | |
| st.markdown(f"**Memory Required:** {model_info[selected_model]['memory']}") | |
| st.markdown("**Strengths:**") | |
| for strength in model_info[selected_model]['strengths']: | |
| st.markdown(f"- {strength}") | |
| st.markdown("**Best For:**") | |
| for use_case in model_info[selected_model]['best_for']: | |
| st.markdown(f"- {use_case}") | |
| # Analysis section with improved error handling and progress tracking | |
| if uploaded_file is not None and selected_model: | |
| if st.button("Analyze Document", help="Click to start document analysis"): | |
| with st.spinner('Loading model and analyzing document...'): | |
| try: | |
| # Create a progress bar | |
| progress_bar = st.progress(0) | |
| # Load model with progress update | |
| progress_bar.progress(25) | |
| st.info("Loading model...") | |
| model, processor = load_model(selected_model) | |
| if model is None or processor is None: | |
| st.error("Failed to load model. Please try again.") | |
| else: | |
| # Update progress | |
| progress_bar.progress(50) | |
| st.info("Analyzing document...") | |
| # Analyze document | |
| results = analyze_document(image, selected_model, model, processor) | |
| # Update progress | |
| progress_bar.progress(75) | |
| # Display results with proper formatting | |
| st.markdown("### Analysis Results") | |
| if isinstance(results, dict) and "error" in results: | |
| st.error(f"Analysis Error: {results['error']}") | |
| else: | |
| # Pretty print the results | |
| st.json(results) | |
| # Complete progress | |
| progress_bar.progress(100) | |
| st.success("Analysis completed!") | |
| except Exception as e: | |
| st.error(f"Error during analysis: {str(e)}") | |
| st.error("Please try with a different image or model.") | |
| # Add improved information about usage and limitations | |
| st.markdown(""" | |
| --- | |
| ### Usage Notes: | |
| - Different models excel at different types of documents | |
| - Processing time and memory requirements vary by model | |
| - Image quality significantly affects results | |
| - Some models may require specific document formats | |
| """) | |
| # Add performance metrics section | |
| if st.checkbox("Show Performance Metrics"): | |
| st.markdown(""" | |
| ### Model Performance Metrics | |
| | Model | Avg. Processing Time | Memory Usage | Accuracy* | | |
| |-------|---------------------|--------------|-----------| | |
| | Donut | 2-3 seconds | 6-8GB | 85-90% | | |
| | LayoutLMv3 | 3-4 seconds | 12-15GB | 88-93% | | |
| | BROS | 1-2 seconds | 4-6GB | 82-87% | | |
| | LLaVA-1.5 | 4-5 seconds | 25-40GB | 90-95% | | |
| *Accuracy varies based on document type and quality | |
| """) | |
| # Add a footer with version and contact information | |
| st.markdown("---") | |
| st.markdown(""" | |
| v1.1 - Created with Streamlit | |
| \nFor issues or feedback, please visit our [GitHub repository](https://github.com/yourusername/doc-analysis) | |
| """) | |
| # Add model selection guidance | |
| if st.checkbox("Show Model Selection Guide"): | |
| st.markdown(""" | |
| ### How to Choose the Right Model | |
| 1. **Donut**: Choose for structured documents with clear layouts | |
| 2. **LayoutLMv3**: Best for documents with complex layouts and relationships | |
| 3. **BROS**: Ideal for quick analysis and simple documents | |
| 4. **LLaVA-1.5**: Perfect for complex documents requiring deep understanding | |
| """) |