Spaces:
Sleeping
Sleeping
File size: 14,214 Bytes
24a7f55 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 |
"""
Multimodal Processor module for the Perception & Understanding Layer.
This module handles the analysis and understanding of web page content
using multimodal large foundation models (LFMs).
"""
import base64
import logging
import os
from typing import Dict, Any, Optional, List
import httpx
import cv2
import numpy as np
import pytesseract
from PIL import Image
from io import BytesIO
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class MultimodalProcessor:
"""
Processes and analyzes web page content using multimodal LFMs.
This class integrates various foundation models to understand
text, images, and their relationships on web pages.
"""
def __init__(self):
"""Initialize the MultimodalProcessor."""
self.vision_model = os.environ.get("VISION_MODEL", "gpt-4-vision-preview")
self.text_model = os.environ.get("TEXT_MODEL", "gpt-4-turbo")
self.openai_client = None
self.anthropic_client = None
self.gemini_client = None
# OCR settings
self.ocr_config = '--oem 3 --psm 11'
logger.info("MultimodalProcessor instance created")
async def initialize(self):
"""Initialize clients and resources."""
# Import API clients here to avoid circular imports
try:
import openai
import anthropic
import google.generativeai as genai
# Initialize OpenAI client
self.openai_client = openai.AsyncClient(
api_key=os.environ.get("OPENAI_API_KEY")
)
# Initialize Anthropic client
self.anthropic_client = anthropic.Anthropic(
api_key=os.environ.get("ANTHROPIC_API_KEY")
)
# Initialize Google Gemini client
genai.configure(api_key=os.environ.get("GEMINI_API_KEY"))
self.gemini_client = genai
logger.info("All LFM clients initialized successfully")
return True
except Exception as e:
logger.error(f"Error initializing LFM clients: {str(e)}")
return False
async def analyze_page(self, screenshot_bytes, dom_text, task_goal):
"""
Analyze a web page using both visual and textual content.
Args:
screenshot_bytes: PNG image bytes of the screenshot
dom_text: Text representation of the DOM
task_goal: Description of the current task goal
Returns:
Dict: Analysis results including identified elements and actions
"""
try:
# Parallel processing of both visual and text analysis
import asyncio
vision_task = self.analyze_image(screenshot_bytes, task_goal)
text_task = self.analyze_text(dom_text, task_goal)
# Wait for both analyses to complete
vision_analysis, text_analysis = await asyncio.gather(vision_task, text_task)
# Synthesize the results
understanding = await self.synthesize_understanding(vision_analysis, text_analysis, task_goal)
return understanding
except Exception as e:
logger.error(f"Error analyzing page: {str(e)}")
return {"error": str(e)}
async def analyze_image(self, image_bytes, task_goal):
"""
Analyze an image using a multimodal vision model.
Args:
image_bytes: PNG image bytes
task_goal: Description of the current task goal
Returns:
Dict: Vision model analysis results
"""
try:
# Perform OCR on the image
ocr_results = await self._extract_text_from_image(image_bytes)
# Encode image to base64 for API
base64_image = base64.b64encode(image_bytes).decode('utf-8')
# Determine which LFM client to use
if self.openai_client and "gpt" in self.vision_model:
response = await self._analyze_with_openai_vision(base64_image, task_goal, ocr_results)
elif self.anthropic_client and "claude" in self.vision_model:
response = await self._analyze_with_anthropic_vision(base64_image, task_goal, ocr_results)
elif self.gemini_client and "gemini" in self.vision_model:
response = await self._analyze_with_gemini_vision(base64_image, task_goal, ocr_results)
else:
raise ValueError(f"Unsupported vision model: {self.vision_model}")
return response
except Exception as e:
logger.error(f"Error in image analysis: {str(e)}")
return {"error": str(e)}
async def _extract_text_from_image(self, image_bytes):
"""
Extract text from an image using OCR.
Args:
image_bytes: PNG image bytes
Returns:
str: Extracted text
"""
try:
# Convert bytes to numpy array
nparr = np.frombuffer(image_bytes, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
# Preprocess the image for better OCR results
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# Perform OCR
text = pytesseract.image_to_string(thresh, config=self.ocr_config)
return text
except Exception as e:
logger.error(f"OCR error: {str(e)}")
return ""
async def _analyze_with_openai_vision(self, base64_image, task_goal, ocr_text):
"""Use OpenAI's vision model for analysis."""
prompt = f"""
Analyze this web page screenshot in the context of the following task:
Task: {task_goal}
OCR extracted text: {ocr_text}
Identify:
1. Main UI elements visible (buttons, forms, links, etc.)
2. Their positions and descriptions
3. Any obstacles to completing the task
4. Recommended actions to progress the task
Return the analysis as a structured JSON object.
"""
response = await self.openai_client.chat.completions.create(
model=self.vision_model,
messages=[
{"role": "system", "content": "You are a web UI analyzer that identifies elements and actions."},
{"role": "user", "content": [
{"type": "text", "text": prompt},
{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64_image}"}}
]}
],
response_format={"type": "json_object"}
)
return response.choices[0].message.content
async def _analyze_with_anthropic_vision(self, base64_image, task_goal, ocr_text):
"""Use Anthropic's Claude model for analysis."""
prompt = f"""
Analyze this web page screenshot in the context of the following task:
Task: {task_goal}
OCR extracted text: {ocr_text}
Identify:
1. Main UI elements visible (buttons, forms, links, etc.)
2. Their positions and descriptions
3. Any obstacles to completing the task
4. Recommended actions to progress the task
Return the analysis as a structured JSON object.
"""
response = await self.anthropic_client.messages.create(
model="claude-3-opus-20240229",
max_tokens=2000,
messages=[
{"role": "user", "content": [
{"type": "text", "text": prompt},
{"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": base64_image}}
]}
]
)
return response.content[0].text
async def _analyze_with_gemini_vision(self, base64_image, task_goal, ocr_text):
"""Use Google's Gemini Vision model for analysis."""
prompt = f"""
Analyze this web page screenshot in the context of the following task:
Task: {task_goal}
OCR extracted text: {ocr_text}
Identify:
1. Main UI elements visible (buttons, forms, links, etc.)
2. Their positions and descriptions
3. Any obstacles to completing the task
4. Recommended actions to progress the task
Return the analysis as a structured JSON object.
"""
# Convert base64 to image for Gemini
image_bytes = base64.b64decode(base64_image)
image = Image.open(BytesIO(image_bytes))
# Generate content with Gemini
generation_config = self.gemini_client.types.GenerationConfig(
temperature=0.2,
response_mime_type="application/json",
)
model = self.gemini_client.GenerativeModel('gemini-pro-vision')
response = model.generate_content(
[
prompt,
image,
],
generation_config=generation_config
)
return response.text
async def analyze_text(self, dom_text, task_goal):
"""
Analyze text content of a DOM using LFMs.
Args:
dom_text: Text representation of the DOM
task_goal: Description of the current task goal
Returns:
Dict: Analysis results including identified elements and structures
"""
try:
prompt = f"""
Analyze this web page DOM text in the context of the following task:
Task: {task_goal}
DOM Text:
{dom_text[:10000]} # Limit size to avoid token limits
Identify:
1. Main interactive elements (buttons, forms, links, etc.)
2. Their IDs, classes, and XPaths where available
3. Page structure and hierarchy
4. Any obstacles to completing the task
5. Recommended actions to progress the task
Return the analysis as a structured JSON object.
"""
if self.openai_client:
response = await self.openai_client.chat.completions.create(
model=self.text_model,
messages=[
{"role": "system", "content": "You are a web DOM analyzer that identifies elements and structures."},
{"role": "user", "content": prompt}
],
response_format={"type": "json_object"}
)
return response.choices[0].message.content
elif self.anthropic_client:
response = await self.anthropic_client.messages.create(
model="claude-3-sonnet-20240229",
max_tokens=2000,
messages=[
{"role": "user", "content": prompt}
]
)
return response.content[0].text
else:
raise ValueError("No suitable text model client available")
except Exception as e:
logger.error(f"Error in text analysis: {str(e)}")
return {"error": str(e)}
async def synthesize_understanding(self, vision_analysis, text_analysis, task_goal):
"""
Synthesize the results from visual and textual analysis.
Args:
vision_analysis: Results from image analysis
text_analysis: Results from DOM text analysis
task_goal: Description of the current task goal
Returns:
Dict: Combined understanding with action recommendations
"""
try:
prompt = f"""
Synthesize the following analyses of a web page in the context of this task:
Task: {task_goal}
Vision Analysis: {vision_analysis}
DOM Text Analysis: {text_analysis}
Create a comprehensive understanding of the page that includes:
1. All identified UI elements with their properties
2. The most accurate selectors to target each element
3. The page structure and navigation flow
4. Specific actionable steps to progress the task
5. Any potential challenges and alternative approaches
Return the synthesis as a structured JSON object optimized for a web automation agent.
"""
if self.openai_client:
response = await self.openai_client.chat.completions.create(
model=self.text_model,
messages=[
{"role": "system", "content": "You are a web automation expert that synthesizes analyses into actionable plans."},
{"role": "user", "content": prompt}
],
response_format={"type": "json_object"}
)
return response.choices[0].message.content
elif self.anthropic_client:
response = await self.anthropic_client.messages.create(
model="claude-3-sonnet-20240229",
max_tokens=2000,
messages=[
{"role": "user", "content": prompt}
]
)
return response.content[0].text
else:
raise ValueError("No suitable text model client available")
except Exception as e:
logger.error(f"Error in synthesizing understanding: {str(e)}")
return {"error": str(e)}
|