78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
import os
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import tempfile
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from typing import List, Dict
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import pandas as pd
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# Import your existing functions directly from complete_pipeline.py
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from complete_pipeline import (
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smart_deskew_with_lines,
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extract_all_clients_from_lines,
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)
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def _process_single_image_bytes(blob: bytes, display_name: str) -> List[Dict]:
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"""
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Saves bytes to a temp file (so OpenCV + Google Vision can read it),
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runs your existing pipeline functions, and returns extracted rows.
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"""
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suffix = os.path.splitext(display_name)[1] or ".jpg"
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tmp_path = None
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try:
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with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
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tmp.write(blob)
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tmp_path = tmp.name
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# Uses Google Vision + deskew + post-line grouping
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info = smart_deskew_with_lines(tmp_path, None, clamp_deg=30.0, use_vision=True)
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post_lines = info.get("post_lines", []) if info else []
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rows = extract_all_clients_from_lines(post_lines) if post_lines else []
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# Add source file information (same as your Streamlit app)
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for r in rows:
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r["Source File"] = display_name
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# If nothing parsed, still return a placeholder row to indicate failure (optional)
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if not rows:
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rows.append({
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'Patient Name': "", 'Patient ID': "", 'ICN': "", 'CDT Code': "",
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'Tooth': "", 'Date SVC': "",
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'Billed Amount': "", 'Allowed Amount': "", 'Paid Amount': "",
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'Extraction Success': False, 'Source File': display_name,
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})
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return rows
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finally:
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if tmp_path:
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try:
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os.unlink(tmp_path)
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except Exception:
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pass
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def process_images_to_rows(blobs: List[bytes], filenames: List[str]) -> List[Dict]:
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"""
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Public API used by FastAPI routes.
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blobs: list of image bytes
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filenames: matching names for display / Source File column
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"""
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all_rows: List[Dict] = []
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for blob, name in zip(blobs, filenames):
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rows = _process_single_image_bytes(blob, name)
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all_rows.extend(rows)
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return all_rows
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def rows_to_csv_bytes(rows: List[Dict]) -> bytes:
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"""
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Convert pipeline rows to CSV bytes (for frontend to consume as a table).
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"""
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df = pd.DataFrame(rows)
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# Keep a stable column order if present (mirrors your Excel order)
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desired = [
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'Patient Name', 'Patient ID', 'ICN', 'CDT Code', 'Tooth', 'Date SVC',
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'Billed Amount', 'Allowed Amount', 'Paid Amount',
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'Extraction Success', 'Source File'
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]
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cols = [c for c in desired if c in df.columns] + [c for c in df.columns if c not in desired]
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df = df[cols]
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return df.to_csv(index=False).encode("utf-8")
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