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import easyocr
import numpy as np
import cv2
import re
reader = easyocr.Reader(['en'], gpu=False)
def extract_weight_from_image(pil_img):
try:
img = np.array(pil_img)
max_dim = 1000
height, width = img.shape[:2]
if max(height, width) > max_dim:
scale = max_dim / max(height, width)
img = cv2.resize(img, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA)
results = reader.readtext(img)
print("DEBUG OCR RESULTS:", results)
raw_texts = []
weight_candidates = []
fallback_weight = None
fallback_conf = 0.0
for item in results:
box, content = item # ✅ This fixes the unpacking error
text, conf = content
original = text
cleaned = text.lower().strip()
cleaned = cleaned.replace(",", ".")
cleaned = cleaned.replace("o", "0").replace("O", "0")
cleaned = cleaned.replace("s", "5").replace("S", "5")
cleaned = cleaned.replace("g", "9").replace("G", "6")
cleaned = cleaned.replace("kg", "").replace("kgs", "")
cleaned = re.sub(r"[^0-9\.]", "", cleaned)
raw_texts.append(f"{original} → {cleaned} (conf: {round(conf, 2)})")
if cleaned and cleaned.replace(".", "").isdigit() and not fallback_weight:
fallback_weight = cleaned
fallback_conf = conf
if cleaned.count(".") <= 1 and re.fullmatch(r"\d{2,4}(\.\d{1,3})?", cleaned):
weight_candidates.append((cleaned, conf))
if weight_candidates:
best_weight, best_conf = sorted(weight_candidates, key=lambda x: -x[1])[0]
elif fallback_weight:
best_weight, best_conf = fallback_weight, fallback_conf
else:
return "Not detected", 0.0, "OCR returned nothing useful"
# Remove leading zeros
if "." in best_weight:
int_part, dec_part = best_weight.split(".")
int_part = int_part.lstrip("0") or "0"
best_weight = f"{int_part}.{dec_part}"
else:
best_weight = best_weight.lstrip("0") or "0"
return best_weight, round(best_conf * 100, 2), "\n".join(raw_texts)
except Exception as e:
return f"Error: {str(e)}", 0.0, "OCR failed"
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