File size: 2,466 Bytes
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Image ingestion for CreditScope.
Converts uploaded images to base64 data-URLs and forwards them directly
to the model for visual understanding. No OCR / Tesseract dependency.
"""
from __future__ import annotations
import base64
import structlog
logger = structlog.get_logger(__name__)
class ImageHandler:
"""
Converts raw image bytes into model-ready data-URL dicts.
All visual analysis is delegated to the model's own vision capabilities.
"""
async def process_images(
self, images: list[bytes], context: str = ""
) -> list[dict]:
"""
Encode every image as a base64 data-URL for the model.
Args:
images: List of raw image bytes.
context: User query context (unused — kept for API compat).
Returns:
List of ``{"type": "image_url", "image_url": {"url": ...}}`` dicts.
"""
processed: list[dict] = []
for img_bytes in images:
try:
processed.append(self._as_image_url(img_bytes))
except Exception as e:
logger.error("image_encoding_failed", error=str(e))
processed.append({
"type": "extracted_data",
"data": {
"error": "Failed to encode image",
"guidance": "The uploaded image could not be processed. "
"Please try a different file.",
},
})
return processed
# ── helpers ────────────────────────────────────────────────────────────
def _as_image_url(self, img_bytes: bytes) -> dict:
mime_type = self._detect_mime_type(img_bytes)
b64 = base64.b64encode(img_bytes).decode()
return {
"type": "image_url",
"image_url": {"url": f"data:{mime_type};base64,{b64}"},
}
@staticmethod
def _detect_mime_type(img_bytes: bytes) -> str:
if img_bytes.startswith(b"\x89PNG\r\n\x1a\n"):
return "image/png"
if img_bytes.startswith(b"\xff\xd8\xff"):
return "image/jpeg"
if img_bytes.startswith((b"II*\x00", b"MM\x00*")):
return "image/tiff"
if img_bytes.startswith(b"%PDF-"):
return "application/pdf"
return "image/jpeg"
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