Create dots-ocr-endpoint-infernece.py

dots-ocr-endpoint-infernece.py

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+import os
+from openai import OpenAI 
+from dotenv import load_dotenv
+import plotly.express as px
+import plotly.graph_objects as go
+import requests
+from PIL import Image
+from io import BytesIO
+import numpy as np
+import json
+
+load_dotenv()
+client = OpenAI(
+	base_url = "",
+	api_key = os.environ["HF_TOKEN"]
+)
+
+prompt = """\
+Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox.
+1. Bbox format: [x1, y1, x2, y2]
+2. Layout Categories: The possible categories are ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title'].
+3. Text Extraction & Formatting Rules:
+    - Picture: For the 'Picture' category, the text field should be omitted.
+    - Formula: Format its text as LaTeX.
+    - Table: Format its text as HTML.
+    - All Others (Text, Title, etc.): Format their text as Markdown.
+4. Constraints:
+    - The output text must be the original text from the image, with no translation.
+    - All layout elements must be sorted according to human reading order.
+5. Final Output: The entire output must be a single JSON object.\
+"""
+
+chat_completion = client.chat.completions.create(
+	model = "rednote-hilab/dots.ocr",
+	messages = [
+		{
+			"role": "user",
+			"content": [
+				{
+					"type": "image_url",
+					"image_url": {
+						"url": "https://github.com/rednote-hilab/dots.ocr/blob/master/demo/demo_image1.jpg?raw=true"
+					}
+				},
+				{
+					"type": "text",
+					"text": prompt,
+				}
+			]
+		}
+	],
+	stream = True,
+)
+
+text = ""
+for message in chat_completion:
+	text = text + message.choices[0].delta.content
+
+annotations = json.loads(text)
+
+# Load image
+url = "https://github.com/rednote-hilab/dots.ocr/blob/master/demo/demo_image1.jpg?raw=true"
+response = requests.get(url)
+img = Image.open(BytesIO(response.content))
+img_array = np.array(img)
+
+# Enhanced color mapping for different categories
+category_colors = {
+    'Title': '#FF6B6B',
+    'Section-header': '#4ECDC4', 
+    'Text': '#45B7D1',
+    'Picture': '#96CEB4',
+    'Table': '#FFEAA7',
+    'Formula': '#DDA0DD',
+    'Caption': '#98D8C8',
+    'List-item': '#F7DC6F',
+    'Footnote': '#BB8FCE',
+    'Page-header': '#85C1E9',
+    'Page-footer': '#F8C471'
+}
+
+# Create figure with enhanced settings
+fig = px.imshow(img_array, aspect='equal')
+
+# Enhanced layout configuration
+fig.update_layout(
+    title={
+        'text': "Interactive OCR Layout Analysis",
+        'x': 0.5,
+        'xanchor': 'center',
+        'font': {'size': 18, 'family': 'Arial Black'}
+    },
+    dragmode="pan",  # Enable panning
+    hovermode="closest",
+    margin=dict(l=20, r=20, t=60, b=20),
+    showlegend=True,
+    legend=dict(
+        orientation="v",
+        yanchor="top",
+        y=1,
+        xanchor="left",
+        x=1.02,
+        bgcolor="rgba(255,255,255,0.8)",
+        bordercolor="rgba(0,0,0,0.2)",
+        borderwidth=1
+    ),
+    plot_bgcolor='white',
+    paper_bgcolor='white'
+)
+
+# Track categories for legend
+added_categories = set()
+
+# Add enhanced bounding boxes with category-based colors
+for i, ann in enumerate(annotations):
+    x1, y1, x2, y2 = ann['bbox']
+    category = ann.get('category', 'Unknown')
+    color = category_colors.get(category, '#FF4444')
+    
+    # Enhanced bounding box with category-specific styling
+    line_width = 3 if category in ['Title', 'Section-header'] else 2
+    opacity = 0.8 if category == 'Picture' else 1.0
+    
+    fig.add_shape(
+        type="rect",
+        x0=x1, y0=y1, x1=x2, y1=y2,
+        line=dict(color=color, width=line_width),
+        opacity=opacity
+    )
+    
+    # Enhanced hover information with better formatting
+    text_content = ann.get('text', 'No text available')
+    if len(text_content) > 200:
+        text_content = text_content[:200] + "..."
+    
+    # Format hover text based on category
+    if category == 'Formula':
+        hover_text = f"<b>🔢 {category}</b><br><i>{text_content}</i>"
+    elif category == 'Picture':
+        hover_text = f"<b>🖼️ {category}</b><br>Image element"
+    elif category == 'Table':
+        hover_text = f"<b>📊 {category}</b><br>{text_content}"
+    elif category == 'Title':
+        hover_text = f"<b>📋 {category}</b><br><b>{text_content}</b>"
+    else:
+        hover_text = f"<b>📄 {category}</b><br>{text_content}"
+    
+    # Add bbox dimensions to hover
+    width = x2 - x1
+    height = y2 - y1
+    hover_text += f"<br><br><i>Box: {width:.0f}×{height:.0f}px</i>"
+    
+    # Create hover point with legend entry
+    show_legend = category not in added_categories
+    if show_legend:
+        added_categories.add(category)
+    
+    fig.add_trace(go.Scatter(
+        x=[(x1 + x2) / 2],
+        y=[(y1 + y2) / 2],
+        mode="markers",
+        marker=dict(
+            size=20,
+            opacity=0,  # Invisible marker
+            color=color
+        ),
+        text=[hover_text],
+        hoverinfo="text",
+        hovertemplate="%{text}<extra></extra>",
+        name=category,
+        showlegend=show_legend,
+        legendgroup=category
+    ))
+
+# Enhanced axes configuration
+fig.update_xaxes(
+    showticklabels=False,
+    showgrid=False,
+    zeroline=False
+)
+fig.update_yaxes(
+    showticklabels=False,
+    showgrid=False,
+    zeroline=False,
+    scaleanchor="x",
+    scaleratio=1
+)
+
+# Add custom controls info
+fig.add_annotation(
+    text="💡 Hover over colored boxes to see content • Pan: drag • Zoom: scroll",
+    xref="paper", yref="paper",
+    x=0.5, y=-0.05,
+    showarrow=False,
+    font=dict(size=12, color="gray"),
+    xanchor="center"
+)
+
+# Display statistics
+total_elements = len(annotations)
+category_counts = {}
+for ann in annotations:
+    cat = ann.get('category', 'Unknown')
+    category_counts[cat] = category_counts.get(cat, 0) + 1
+
+print(f"📊 Layout Analysis Complete!")
+print(f"Total elements detected: {total_elements}")
+print("Category breakdown:")
+for cat, count in sorted(category_counts.items()):
+    print(f"  • {cat}: {count}")
+
+fig.show()