fix(cleanup): final pyflakes cleanup across 4 files

image_type_classifier.py: remove unused arr_hsv, hsv_img, colorsys import
report_exporter.py: remove unused struct, datetime; fix dead y variable
and add() function; fix bare f-string buf.write(f'0 6\n')
batch_processor.py: remove unused hashlib, BytesIO imports
analyze.py: remove unused 'import time as _time' inside metrics recorder

backend/api/routes/analyze.py

@@ -170,7 +170,6 @@ async def analyze_image(
 
         # Record metrics
         try:
-            import time as _time
             record_analysis(
                 ai_probability=report["summary"]["ai_probability"],
                 classification=report["summary"]["ai_classification"],

backend/services/batch_processor.py

@@ -17,9 +17,7 @@ Limits:
   Processing is sequential to avoid OOM on GPU
 """
 import logging
-import hashlib
 from typing import Dict, Any, List
-from io import BytesIO
 
 logger = logging.getLogger(__name__)
 
@@ -230,4 +228,4 @@ def process_batch(
         "duplicate_pairs":        _find_duplicates(reports),
         "provenance_consistency": _provenance_consistency(reports),
         "individual_reports":     results,
-    }
+    }

backend/services/image_type_classifier.py

@@ -63,10 +63,7 @@ def classify_image_type(image_bytes: bytes, filename: str = "unknown") -> Dict[s
         edge_density = float(np.count_nonzero(edges) / max(edges.size, 1))
 
         # 3. Color saturation
-        arr_hsv      = np.array(img.convert("HSV") if hasattr(img, 'convert') else img)
         try:
-            hsv_img  = img.copy()
-            import colorsys
             rgb_flat = arr.reshape(-1, 3) / 255.0
             # Approximate saturation from RGB
             r, g, b  = rgb_flat[:, 0], rgb_flat[:, 1], rgb_flat[:, 2]

backend/services/report_exporter.py

@@ -27,9 +27,7 @@ import json
 import csv
 import io
 import logging
-import struct
 import zlib
-from datetime import datetime
 from typing import Dict, Any
 
 logger = logging.getLogger(__name__)
@@ -160,13 +158,7 @@ class _PDFWriter:
         platform_o  = platform.get("predicted_platform", "N/A")
         c2pa_status = c2pa.get("provenance_status", "N/A")
 
-        # Build page 1 content
-        lines = []
-        y = 750
-        def add(text, x=50, bold=False):
-            lines.append((0, 0, ""))  # placeholder — we'll use Td chaining below
-
-        # We'll build a flat text stream manually
+        # Build page 1 content stream
         text_ops = []
         text_ops.append(b"BT")
         text_ops.append(b"/F1 16 Tf")
@@ -279,7 +271,7 @@ class _PDFWriter:
 
         xref_offset = buf.tell()
         buf.write(b"xref\n")
-        buf.write(f"0 6\n".encode())
+        buf.write(b"0 6\n")
         buf.write(b"0000000000 65535 f \n")
         for i in range(1, 6):
             buf.write(f"{offsets[i]:010d} 00000 n \n".encode())