Update app.py

Streamlit app that compares different document understanding models

app.py

@@ -1,4 +1,140 @@
 import streamlit as st
+from PIL import Image
+import torch
+from transformers import (
+    DonutProcessor, 
+    VisionEncoderDecoderModel,
+    LayoutLMv3Processor, 
+    LayoutLMv3ForSequenceClassification,
+    BrosProcessor,
+    BrosForTokenClassification,
+    LlavaProcessor,
+    LlavaForConditionalGeneration
+)
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+def load_model(model_name):
+    """Load the selected model and processor"""
+    if model_name == "Donut":
+        processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base")
+        model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base")
+    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
+
+def analyze_document(image, model_name, model, processor):
+    """Analyze document using selected model"""
+    try:
+        # Process image according to model requirements
+        if model_name == "Donut":
+            inputs = processor(image, return_tensors="pt")
+            outputs = model.generate(**inputs)
+            result = processor.decode(outputs[0], skip_special_tokens=True)
+        elif model_name == "LayoutLMv3":
+            inputs = processor(image, return_tensors="pt")
+            outputs = model(**inputs)
+            result = outputs.logits
+        # Add similar processing for other models
+        
+        return result
+    except Exception as e:
+        st.error(f"Error analyzing document: {str(e)}")
+        return None
+
+# Set page config
+st.set_page_config(page_title="Document Analysis Comparison", layout="wide")
+
+# 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
+    uploaded_file = st.file_uploader("Choose a document image", type=['png', 'jpg', 'jpeg', 'pdf'])
+    
+    if uploaded_file is not None:
+        # Display uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Document', use_column_width=True)
+
+with col2:
+    # Model selection
+    model_info = {
+        "Donut": {
+            "description": "Best for structured OCR and document format understanding",
+            "memory": "6-8GB",
+            "strengths": ["Structured OCR", "Memory efficient", "Good with fixed formats"]
+        },
+        "LayoutLMv3": {
+            "description": "Strong layout understanding with reasoning capabilities",
+            "memory": "12-15GB",
+            "strengths": ["Layout understanding", "Reasoning", "Pre-trained knowledge"]
+        },
+        "BROS": {
+            "description": "Memory efficient with fast inference",
+            "memory": "4-6GB",
+            "strengths": ["Fast inference", "Memory efficient", "Easy fine-tuning"]
+        },
+        "LLaVA-1.5": {
+            "description": "Comprehensive OCR with strong reasoning",
+            "memory": "25-40GB",
+            "strengths": ["Strong reasoning", "Zero-shot capable", "Visual understanding"]
+        }
+    }
+    
+    selected_model = st.selectbox(
+        "Select Model",
+        list(model_info.keys())
+    )
+    
+    # Display model information
+    st.write("### Model Details")
+    st.write(f"**Description:** {model_info[selected_model]['description']}")
+    st.write(f"**Memory Required:** {model_info[selected_model]['memory']}")
+    st.write("**Strengths:**")
+    for strength in model_info[selected_model]['strengths']:
+        st.write(f"- {strength}")
+
+# Analysis section
+if uploaded_file is not None and selected_model:
+    if st.button("Analyze Document"):
+        with st.spinner('Loading model and analyzing document...'):
+            try:
+                # Load model and processor
+                model, processor = load_model(selected_model)
+                
+                # Analyze document
+                results = analyze_document(image, selected_model, model, processor)
+                
+                # Display results
+                st.write("### Analysis Results")
+                st.json(results)
+                
+            except Exception as e:
+                st.error(f"Error during analysis: {str(e)}")
+
+# Add information about usage and limitations
+st.markdown("""
+---
+### Notes:
+- Different models may perform better for different types of documents
+- Processing time and memory requirements vary by model
+- Results may vary based on document quality and format
+""")
+
+# Add a footer with version information
+st.markdown("---")
+st.markdown("v1.0 - Created with Streamlit")