Delete utils.py

utils.py

@@ -1,140 +0,0 @@
-import numpy as np
-from datasets import load_metric
-from PIL import ImageDraw, ImageFont
-import pandas as pd
-
-
-metric = load_metric("seqeval")
-
-
-def unnormalize_box(bbox, width, height):
-    return [
-        width * (bbox[0] / 1000),
-        height * (bbox[1] / 1000),
-        width * (bbox[2] / 1000),
-        height * (bbox[3] / 1000)
-    ]
-
-
-def normalize_box(bbox, width, height):
-    return [
-        int((bbox[0] / width) * 1000),
-        int((bbox[1] / height) * 1000),
-        int((bbox[2] / width) * 1000),
-        int((bbox[3] / height) * 1000)
-    ]
-
-
-def draw_output(image, true_predictions, true_boxes):
-    def iob_to_label(label):
-        label = label
-        if not label:
-            return 'other'
-        return label
-
-    # width, height = image.size
-
-    # predictions = logits.argmax(-1).squeeze().tolist()
-    # is_subword = np.array(offset_mapping)[:,0] != 0
-    # true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
-    # true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
-
-    # draw
-    draw = ImageDraw.Draw(image)
-    font = ImageFont.load_default()
-
-    for prediction, box in zip(true_predictions, true_boxes):
-        predicted_label = iob_to_label(prediction).lower()
-        draw.rectangle(box, outline='red')
-        draw.text((box[0] + 10, box[1] - 10),
-                  text=predicted_label, fill='red', font=font)
-
-    return image
-
-
-def create_df(true_texts,
-              true_predictions,
-              chosen_labels=['SHOP_NAME', 'ADDR', 'TITLE', 'PHONE',
-                             'PRODUCT_NAME', 'AMOUNT', 'UNIT', 'UPRICE', 'SUB_TPRICE', 'UDISCOUNT',
-                             'TAMOUNT', 'TPRICE', 'FPRICE', 'TDISCOUNT',
-                             'RECEMONEY', 'REMAMONEY',
-                             'BILLID', 'DATETIME', 'CASHIER']
-              ):
-
-    data = {'text': [], 'class_label': [], 'product_id': []}
-    product_id = -1
-    for text, prediction in zip(true_texts, true_predictions):
-        if prediction not in chosen_labels:
-            continue
-
-        if prediction == 'PRODUCT_NAME':
-            product_id += 1
-            
-
-        if prediction in ['AMOUNT', 'UNIT', 'UDISCOUNT', 'UPRICE', 'SUB_TPRICE',
-                          'UDISCOUNT', 'TAMOUNT', 'TPRICE', 'FPRICE', 'TDISCOUNT',
-                          'RECEMONEY', 'REMAMONEY']:
-            text = reformat(text)
-
-
-        if prediction in ['AMOUNT', 'SUB_TPRICE', 'UPRICE', 'PRODUCT_NAME']:
-            data['product_id'].append(product_id)
-        else:
-            data['product_id'].append('')
-
-
-        data['class_label'].append(prediction)
-        data['text'].append(text)
-
-
-    df = pd.DataFrame(data)
-
-    return df
-
-
-def reformat(text: str):
-    try:
-        text = text.replace('.', '').replace(',', '').replace(':', '').replace('/', '').replace('|', '').replace(
-            '\\', '').replace(')', '').replace('(', '').replace('-', '').replace(';', '').replace('_', '')
-        return int(text)
-    except:
-        return text
-
-def find_product(product_name, df):
-    product_name = product_name.lower()
-    product_df = df[df['class_label'] == 'PRODUCT_NAME']
-    mask = product_df['text'].str.lower().str.contains(product_name, case=False, na=False)
-    if mask.any():
-        product_id = product_df.loc[mask, 'product_id'].iloc[0]
-        product_info = df[df['product_id'] == product_id]
-        
-        prod_name = product_info.loc[product_info['class_label'] == 'PRODUCT_NAME', 'text'].iloc[0]
-            
-        try:
-            amount = product_info.loc[product_info['class_label'] == 'AMOUNT', 'text'].iloc[0]
-        except:
-            print("Error: cannot find amount")
-            amount = ''
-            
-        try:
-            uprice = product_info.loc[product_info['class_label'] == 'UPRICE', 'text'].iloc[0]
-        except:
-            print("Error: cannot find unit price")
-            uprice = ''
-            
-        try:
-            sub_tprice = product_info.loc[product_info['class_label'] == 'SUB_TPRICE', 'text'].iloc[0]
-        except:
-            print("Error: cannot find sub total price")
-            sub_tprice = ''
-            
-        #print("Sản phẩm: ", product_info.loc[product_info['class_label'] == 'PRODUCT_NAME', 'text'].iloc[0])
-        #print("Số lượng: ", product_info.loc[product_info['class_label'] == 'AMOUNT', 'text'].iloc[0])
-        #print("Đơn giá: ", product_info.loc[product_info['class_label'] == 'UPRICE', 'text'].iloc[0])
-        #print("Thành tiền: ", product_info.loc[product_info['class_label'] == 'SUB_TPRICE', 'text'].iloc[0])
-        return f"Sản phẩm: {prod_name}\n Số lượng: {amount}\n Đơn giá: {uprice}\n Thành tiền: {sub_tprice}"
-    else:
-        #print("Không tìm thấy item nào phù hợp.")
-        return "Không tìm thấy item nào phù hợp."
-    #return result = product_df['text'].str.contains(product_name, case=False, na=False).any()
-    #return product_df[product_df['text'].str.contains(product_name, case=False, na=False)]