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import os
import json
import logging
import numpy as np
from fastapi import APIRouter, UploadFile, File, BackgroundTasks, HTTPException, Request
from fastapi.responses import JSONResponse
from modules.element_processing import process_screenshot
from pydantic import BaseModel
from typing import List, Dict, Optional, Any, Tuple
# Custom JSON encoder to handle numpy types
class NumpyEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
return super(NumpyEncoder, self).default(obj)
logger = logging.getLogger(__name__)
router = APIRouter()
class ElementResponse(BaseModel):
code: str
type: str
text_content: Optional[str] = None
object_label: Optional[str] = None
bbox_normalized: List[float]
bbox_pixels: Optional[List[int]] = None
center_x: int
center_y: int
class ScreenshotRequest(BaseModel):
screen_width: int
screen_height: int
class ApiResponse(BaseModel):
output: Any
image_url: str
class ScreenshotDataURIRequest(BaseModel):
image_data_uri: str
screen_width: Optional[int] = None
screen_height: Optional[int] = None
@router.post("/process_screenshot/")
async def process_screenshot_endpoint(
request: Request,
background_tasks: BackgroundTasks,
file: UploadFile = File(...),
screen_width: int = None,
screen_height: int = None
) -> JSONResponse:
"""
Process a screenshot image to identify UI elements, assign codes, and return element data.
Args:
request: FastAPI request object
background_tasks: FastAPI background tasks
file: The uploaded screenshot image file
screen_width: Target screen width (for coordinate scaling)
screen_height: Target screen height (for coordinate scaling)
Returns:
JSON with elements array and annotated image URL
"""
start_time = time.time()
logger.info(f"Processing screenshot: {file.filename}")
try:
# Read image data
image_data = await file.read()
if not image_data:
raise HTTPException(status_code=400, detail="Empty image data")
# Process the screenshot
elements, image_path = await process_screenshot(image_data, background_tasks, screen_width, screen_height)
# Create full URL for the image
base_url = str(request.base_url).rstrip('/')
image_url = f"{base_url}/{image_path}" if image_path else ""
# Log processing time
processing_time = time.time() - start_time
logger.info(f"Screenshot processed in {processing_time:.2f} seconds")
# Use the custom JSON encoder to handle numpy types
response_data = {
"output": elements,
"image_url": image_url
}
return JSONResponse(content=json.loads(json.dumps(response_data, cls=NumpyEncoder)))
except Exception as e:
logger.error(f"Error processing screenshot: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
@router.post("/process_screenshot_string/")
async def process_screenshot_string_endpoint(
request: Request,
background_tasks: BackgroundTasks,
file: UploadFile = File(...),
screen_width: int = None,
screen_height: int = None
) -> JSONResponse:
"""
Process a screenshot and return elements as a formatted string.
Each line represents an icon in the format:
'icon CODE: {'type': 'text/object', 'centerX': x, 'centerY': y, 'content': 'Text'}'
Args:
request: FastAPI request object
background_tasks: FastAPI background tasks
file: The uploaded screenshot image file
screen_width: Target screen width (for coordinate scaling)
screen_height: Target screen height (for coordinate scaling)
Returns:
JSON with string output and annotated image URL
"""
start_time = time.time()
logger.info(f"Processing screenshot for string output: {file.filename}")
try:
# Read image data
image_data = await file.read()
if not image_data:
raise HTTPException(status_code=400, detail="Empty image data")
# Process the screenshot
elements, image_path = await process_screenshot(image_data, background_tasks, screen_width, screen_height)
# Create full URL for the image
base_url = str(request.base_url).rstrip('/')
image_url = f"{base_url}/{image_path}" if image_path else ""
# Format elements as string
result_lines = []
for element in elements:
# Determine content based on element type
if element["type"] == "text":
content_value = element.get("text_content", "")
else:
content_value = element.get("object_label", "")
# Format the element string
element_str = (f"icon {element['code']}: {{"
f"'type': '{element['type']}', "
f"'centerX': {element['center_x']}, "
f"'centerY': {element['center_y']}, "
f"'content': '{content_value}'}}")
result_lines.append(element_str)
# Join all lines
result_string = "\n".join(result_lines)
# Log processing time
processing_time = time.time() - start_time
logger.info(f"Screenshot processed for string output in {processing_time:.2f} seconds")
# Use the custom JSON encoder to handle numpy types
response_data = {
"output": result_string,
"image_url": image_url
}
return JSONResponse(content=response_data)
except Exception as e:
logger.error(f"Error processing screenshot for string output: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
@router.post("/process_screenshot_data_uri/")
async def process_screenshot_data_uri_endpoint(
request: Request,
background_tasks: BackgroundTasks,
data: ScreenshotDataURIRequest
) -> JSONResponse:
"""
Process a screenshot from data URI to identify UI elements, assign codes, and return element data.
Args:
request: FastAPI request object
background_tasks: FastAPI background tasks
data: JSON containing image data URI and screen dimensions
Returns:
JSON with string output and annotated image URL
"""
start_time = time.time()
logger.info("Processing screenshot from data URI")
try:
# Extract image data from data URI
import base64
if not data.image_data_uri.startswith('data:image'):
raise HTTPException(status_code=400, detail="Invalid data URI format")
# Split the header and the base64 data
header, encoded = data.image_data_uri.split(",", 1)
image_data = base64.b64decode(encoded)
if not image_data:
raise HTTPException(status_code=400, detail="Empty image data")
# Process the screenshot
elements, image_path = await process_screenshot(
image_data,
background_tasks,
data.screen_width,
data.screen_height
)
# Create full URL for the image
base_url = str(request.base_url).rstrip('/')
image_url = f"{base_url}/{image_path}" if image_path else ""
# Format elements as string
result_lines = []
for element in elements:
# Determine content based on element type
if element["type"] == "text":
content_value = element.get("text_content", "")
else:
content_value = element.get("object_label", "")
# Format the element string
element_str = (f"icon {element['code']}: {{"
f"'type': '{element['type']}', "
f"'centerX': {element['center_x']}, "
f"'centerY': {element['center_y']}, "
f"'content': '{content_value}'}}")
result_lines.append(element_str)
# Join all lines
result_string = "\n".join(result_lines)
# Log processing time
processing_time = time.time() - start_time
logger.info(f"Screenshot processed for string output in {processing_time:.2f} seconds")
# Use the custom JSON encoder to handle numpy types
response_data = {
"output": result_string,
"image_url": image_url
}
return JSONResponse(content=response_data)
except Exception as e:
logger.error(f"Error processing screenshot from data URI: {str(e)}")
raise HTTPException(status_code=500, detail=str(e)) |