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"""
VisionOCRAgent for SPARKNET
Handles OCR and document vision tasks using Ollama's llava model.
Extracts text from images, PDFs, diagrams, and complex documents.
"""
import base64
from pathlib import Path
from typing import Optional, Dict, Any
from loguru import logger
from langchain_ollama import ChatOllama
from langchain_core.messages import HumanMessage
class VisionOCRAgent:
"""
Specialized agent for vision-based OCR tasks.
Uses llava vision-language model for document analysis.
"""
def __init__(self, model_name: str = "llava:7b", base_url: str = "http://localhost:11434"):
"""
Initialize VisionOCRAgent.
Args:
model_name: Ollama vision model to use (default: llava:7b)
base_url: Ollama service URL
"""
self.model_name = model_name
self.base_url = base_url
# Initialize Ollama vision model
self.vision_llm = ChatOllama(
model=model_name,
base_url=base_url,
temperature=0.1, # Low temperature for accurate extraction
)
logger.info(f"Initialized VisionOCRAgent with model: {model_name}")
def _encode_image(self, image_path: str) -> str:
"""
Encode image to base64 for llava.
Args:
image_path: Path to image file
Returns:
Base64 encoded image string
"""
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
async def extract_text_from_image(
self,
image_path: str,
preserve_formatting: bool = True
) -> str:
"""
Extract text from an image using vision model.
Args:
image_path: Path to image file
preserve_formatting: Whether to preserve document structure
Returns:
Extracted text content
"""
logger.info(f"📷 Extracting text from: {image_path}")
try:
# Prepare prompt based on formatting preference
if preserve_formatting:
prompt = """Extract all text from this image, preserving the original formatting and structure.
Maintain:
- Paragraph breaks and line spacing
- Bullet points and numbered lists
- Section headings and hierarchy
- Table structures if present
Return only the extracted text, formatted as closely as possible to the original."""
else:
prompt = "Extract all text from this image. Return only the text content without any additional commentary."
# Encode image
image_data = self._encode_image(image_path)
# Create message with image
message = HumanMessage(
content=[
{"type": "text", "text": prompt},
{
"type": "image_url",
"image_url": f"data:image/jpeg;base64,{image_data}"
}
]
)
# Get response from vision model
response = await self.vision_llm.ainvoke([message])
extracted_text = response.content
logger.success(f"✅ Extracted {len(extracted_text)} characters from {Path(image_path).name}")
return extracted_text
except Exception as e:
logger.error(f"Failed to extract text from {image_path}: {e}")
raise
async def analyze_diagram(self, image_path: str) -> Dict[str, Any]:
"""
Analyze technical diagrams, flowcharts, and schematics.
Args:
image_path: Path to diagram image
Returns:
Dictionary with diagram analysis
"""
logger.info(f"📊 Analyzing diagram: {image_path}")
try:
prompt = """Analyze this technical diagram in detail. Provide:
1. Type of diagram (flowchart, circuit, organizational chart, etc.)
2. Main components and elements
3. All text labels and annotations
4. Connections and relationships between elements
5. Overall purpose and meaning
Format your response as structured text."""
image_data = self._encode_image(image_path)
message = HumanMessage(
content=[
{"type": "text", "text": prompt},
{
"type": "image_url",
"image_url": f"data:image/jpeg;base64,{image_data}"
}
]
)
response = await self.vision_llm.ainvoke([message])
analysis = response.content
logger.success(f"✅ Analyzed diagram: {Path(image_path).name}")
return {
"diagram_type": "technical_diagram",
"analysis": analysis,
"source": image_path
}
except Exception as e:
logger.error(f"Failed to analyze diagram {image_path}: {e}")
raise
async def extract_table_data(self, image_path: str) -> str:
"""
Extract data from tables in images.
Args:
image_path: Path to image containing table
Returns:
Table data in markdown format
"""
logger.info(f"📋 Extracting table from: {image_path}")
try:
prompt = """Extract the table data from this image.
Format the output as a Markdown table with proper alignment:
- Use | for column separators
- Use | --- | for header separator
- Maintain proper column alignment
- Include all rows and columns
Example format:
| Header 1 | Header 2 | Header 3 |
| --- | --- | --- |
| Data 1 | Data 2 | Data 3 |
Return ONLY the table, no additional text."""
image_data = self._encode_image(image_path)
message = HumanMessage(
content=[
{"type": "text", "text": prompt},
{
"type": "image_url",
"image_url": f"data:image/jpeg;base64,{image_data}"
}
]
)
response = await self.vision_llm.ainvoke([message])
table_markdown = response.content
logger.success(f"✅ Extracted table from {Path(image_path).name}")
return table_markdown
except Exception as e:
logger.error(f"Failed to extract table from {image_path}: {e}")
raise
async def analyze_patent_page(self, image_path: str) -> Dict[str, Any]:
"""
Specialized analysis for patent document pages.
Args:
image_path: Path to patent page image
Returns:
Dictionary with extracted patent information
"""
logger.info(f"📄 Analyzing patent page: {image_path}")
try:
prompt = """Analyze this patent document page. Extract:
1. Patent number or application number (if visible)
2. Title or heading
3. All body text (claims, descriptions, specifications)
4. Figure numbers and captions
5. Any diagrams or technical drawings descriptions
6. Inventor names and assignee information (if visible)
7. Dates (filing date, publication date, etc.)
Preserve the structure and formatting. Return comprehensive extracted content."""
image_data = self._encode_image(image_path)
message = HumanMessage(
content=[
{"type": "text", "text": prompt},
{
"type": "image_url",
"image_url": f"data:image/jpeg;base64,{image_data}"
}
]
)
response = await self.vision_llm.ainvoke([message])
analysis = response.content
logger.success(f"✅ Analyzed patent page: {Path(image_path).name}")
return {
"page_content": analysis,
"source": image_path,
"type": "patent_page"
}
except Exception as e:
logger.error(f"Failed to analyze patent page {image_path}: {e}")
raise
async def identify_handwriting(self, image_path: str) -> str:
"""
Extract handwritten text from images.
Args:
image_path: Path to image with handwritten content
Returns:
Extracted handwritten text
"""
logger.info(f"✍️ Extracting handwriting from: {image_path}")
try:
prompt = """This image contains handwritten text. Please:
1. Carefully read all handwritten content
2. Transcribe the text exactly as written
3. Indicate [unclear] for illegible portions
4. Preserve line breaks and spacing
5. Note any annotations or margin notes
Return only the transcribed text."""
image_data = self._encode_image(image_path)
message = HumanMessage(
content=[
{"type": "text", "text": prompt},
{
"type": "image_url",
"image_url": f"data:image/jpeg;base64,{image_data}"
}
]
)
response = await self.vision_llm.ainvoke([message])
handwriting = response.content
logger.success(f"✅ Extracted handwriting from {Path(image_path).name}")
return handwriting
except Exception as e:
logger.error(f"Failed to extract handwriting from {image_path}: {e}")
raise
def is_available(self) -> bool:
"""
Check if vision model is available.
Returns:
True if model is available, False otherwise
"""
try:
# Try a simple test
import requests
response = requests.get(f"{self.base_url}/api/tags")
if response.status_code == 200:
models = response.json().get("models", [])
return any(self.model_name in model.get("name", "") for model in models)
return False
except Exception as e:
logger.warning(f"Could not check model availability: {e}")
return False
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