Update app.py

app.py

@@ -56,35 +56,64 @@ def app_outputs(uploaded_pdf):
     if not msg.startswith("Error with the PDF"):
     
         # Extraction of image data (text and bounding boxes)
-        dataset, lines, row_indexes, par_boxes, line_boxes = extraction_data_from_image(images)
+        dataset, texts_lines, texts_pars, texts_lines_par, row_indexes, par_boxes, line_boxes, lines_par_boxes = extraction_data_from_image(images)
+        print(dataset)
         # prepare our data in the format of the model
-        encoded_dataset = dataset.map(prepare_inference_features, batched=True, batch_size=64, remove_columns=dataset.column_names)
+        encoded_dataset = dataset.map(prepare_inference_features_paragraph, batched=True, batch_size=64, remove_columns=dataset.column_names)
         custom_encoded_dataset = CustomDataset(encoded_dataset, tokenizer)
         # Get predictions (token level)
         outputs, images_ids_list, chunk_ids, input_ids, bboxes = predictions_token_level(images, custom_encoded_dataset)
-        # Get predictions (paragraph level)
-        probs_bbox, bboxes_list_dict, input_ids_dict_dict, probs_dict_dict, df = predictions_paragraph_level(dataset, outputs, images_ids_list, chunk_ids, input_ids, bboxes)
-        # Get labeled images with paragraphs bounding boxes
-        images = get_labeled_images(dataset, images_ids_list, bboxes_list_dict, probs_dict_dict)
+        # Get predictions (line level)
+        probs_bbox, bboxes_list_dict, input_ids_dict_dict, probs_dict_dict, df = predictions_paragraph_level_gradio(dataset, outputs, images_ids_list, chunk_ids, input_ids, bboxes)
+        # Get labeled images with lines bounding boxes
+        labeled_images = get_labeled_images_gradio(dataset, images_ids_list, bboxes_list_dict, probs_dict_dict)
 
         img_files = list()
-        # get image of PDF without bounding boxes
+        # get image of PDF with bounding boxes
         for i in range(num_images):
             if filename != "files/blank.png": img_file = f"img_{i}_" + filename.replace(".pdf", ".png")
             else: img_file = filename.replace(".pdf", ".png")
-            images[i].save(img_file)
+            labeled_images[i].save(img_file)
             img_files.append(img_file)
 
         if num_images < max_imgboxes:
+            num_true_images = num_images
             img_files += [image_blank]*(max_imgboxes - num_images)
-            images += [Image.open(image_blank)]*(max_imgboxes - num_images)
+            labeled_images += [Image.open(image_blank)]*(max_imgboxes - num_images)
             for count in range(max_imgboxes - num_images):
                 df[num_images + count] = pd.DataFrame()
         else:
+            num_true_images = max_imgboxes
             img_files = img_files[:max_imgboxes]
-            images = images[:max_imgboxes]
+            labeled_images = labeled_images[:max_imgboxes]
             df = dict(itertools.islice(df.items(), max_imgboxes))
 
+        for num_page in range(num_true_images):
+            example = dataset[num_page]
+            df_num_page = df[num_page]
+            width, height = example["images"].size
+
+            # apply same transformations
+            bboxes_par_list = [denormalize_box(normalize_box(upperleft_to_lowerright(bbox), width, height), width, height) for bbox in example['bboxes_par']]
+
+            texts_list = list()
+            for bbox_par, label in zip(df_num_page["bboxes"].tolist(), df_num_page["labels"]):
+                index_par = bboxes_par_list.index(bbox_par)
+                bboxes_lines_par_list = dataset[num_page]["bboxes_lines_par"][index_par]
+                texts_lines_par_list = dataset[num_page]["texts_lines_par"][index_par]
+                boxes, texts = sort_data_wo_labels(bboxes_lines_par_list, texts_lines_par_list)
+                # apply text startegy in function of label
+                if label == "Text" or label == "Caption" or label == "Footnote":
+                    texts = ' '.join(texts)
+                else:
+                    texts = '\n'.join(texts)
+                texts_list.append(texts)
+
+            df[num_page]["Paragraph text"] = texts_list
+
+            cols = ["bboxes", "Paragraph text", "labels"]
+            df[num_page] = df[num_page][cols]
+
         # save 
         csv_files = list()
         for i in range(max_imgboxes):
@@ -93,25 +122,25 @@ def app_outputs(uploaded_pdf):
             df[i].to_csv(csv_file, encoding="utf-8", index=False)
 
     else:  
-        img_files, images, csv_files = [""]*max_imgboxes, [""]*max_imgboxes, [""]*max_imgboxes
+        img_files, labeled_images, csv_files = [""]*max_imgboxes, [""]*max_imgboxes, [""]*max_imgboxes
         img_files[0], img_files[1] = image_blank, image_blank
-        images[0], images[1] = Image.open(image_blank), Image.open(image_blank)
+        labeled_images[0], labeled_images[1] = Image.open(image_blank), Image.open(image_blank)
         csv_file = "csv_wo_content.csv"
         csv_files[0], csv_files[1] = gr.File.update(value=csv_file, visible=True), gr.File.update(value=csv_file, visible=True)
         df, df_empty = dict(), pd.DataFrame()
         df[0], df[1] = df_empty.to_csv(csv_file, encoding="utf-8", index=False), df_empty.to_csv(csv_file, encoding="utf-8", index=False)
     
-    return msg, img_files[0], img_files[1], images[0], images[1], csv_files[0], csv_files[1], df[0], df[1]
+    return msg, img_files[0], img_files[1], labeled_images[0], labeled_images[1], csv_files[0], csv_files[1], df[0], df[1]
 
 # gradio APP
 with gr.Blocks(title="Inference APP for Document Understanding at paragraph level (v1)", css=".gradio-container") as demo:
     gr.HTML("""
     <div style="font-family:'Times New Roman', 'Serif'; font-size:26pt; font-weight:bold; text-align:center;"><h1>Inference APP for Document Understanding at paragraph level (v1)</h1></div>
-    <div style="margin-top: 40px"><p>(02/12/2023) This Inference APP uses the <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512-v4" target="_blank">model LiLT base combined with XLM-RoBERTa base and finetuned on the dataset DocLayNet base at paragraph level</a> (chunk size of 512 tokens).</p></div>
+    <div style="margin-top: 40px"><p>(02/12/2023) This Inference APP uses the <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512" target="_blank">model LiLT base combined with XLM-RoBERTa base and finetuned on the dataset DocLayNet base at paragraph level</a> (chunk size of 512 tokens).</p></div>
     <div><p><a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://arxiv.org/abs/2202.13669" target="_blank">LiLT (Language-Independent Layout Transformer)</a> is a Document Understanding model that uses both layout and text in order to detect labels of bounding boxes. Combined with the model <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/xlm-roberta-base" target="_blank">XML-RoBERTa base</a>, this finetuned model has the capacity to <b>understand any language</b>. Finetuned on the dataset <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/datasets/pierreguillou/DocLayNet-base" target="_blank">DocLayNet base</a>, it can <b>classifly any bounding box (and its OCR text) to 11 labels</b> (Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title).</p></div>
     <div><p>It relies on an external OCR engine to get words and bounding boxes from the document image. Thus, let's run in this APP an OCR engine (<a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://github.com/madmaze/pytesseract#python-tesseract" target="_blank">PyTesseract</a>) to get the bounding boxes, then run LiLT (already fine-tuned on the dataset DocLayNet base at paragraph level) on the individual tokens and then, visualize the result at paragraph level!</p></div>
     <div><p><b>It allows to get all pages of any PDF (of any language) with bounding boxes labeled at paragraph level and the associated dataframes with labeled data (bounding boxes, texts, labels) :-)</b></p></div>
-    <div><p>However, the inference time per page can be high when running the model on CPU due to the number of paragraph predictions to be made. Therefore, to avoid running this APP for too long, <b>only the first 2 pages are processed by this APP</b>. If you want to increase this limit, you can either clone this APP in Hugging Face Space (or run its <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://github.com/piegu/language-models/blob/master/Gradio_inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levelparagraphs_ml512-v4.ipynb" target="_blank">notebook</a> on your own plateform) and change the value of the parameter <code>max_imgboxes</code>, or run the inference notebook "<a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://github.com/piegu/language-models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levelparagraphs_ml512-v4.ipynb" target="_blank">Document AI | Inference at paragraph level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)</a>" on your own platform as it does not have this limit.</p></div>
+    <div><p>However, the inference time per page can be high when running the model on CPU due to the number of paragraph predictions to be made. Therefore, to avoid running this APP for too long, <b>only the first 2 pages are processed by this APP</b>. If you want to increase this limit, you can either clone this APP in Hugging Face Space (or run its <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://github.com/piegu/language-models/blob/master/Gradio_inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levelparagraphs_ml512.ipynb" target="_blank">notebook</a> on your own plateform) and change the value of the parameter <code>max_imgboxes</code>, or run the inference notebook "<a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://github.com/piegu/language-models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levelparagraphs_ml512.ipynb" target="_blank">Document AI | Inference at paragraph level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)</a>" on your own platform as it does not have this limit.</p></div>
     <div style="margin-top: 20px"><p>More information about the DocLayNet datasets, the finetuning of the model and this APP in the following blog posts:</p>
     <ul><li>(02/14/2023) <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://medium.com/@pierre_guillou/document-ai-inference-app-for-document-understanding-at-line-level-a35bbfa98893" target="_blank">Document AI | Inference APP for Document Understanding at line level</a></li><li>(02/10/2023) <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://medium.com/@pierre_guillou/document-ai-document-understanding-model-at-line-level-with-lilt-tesseract-and-doclaynet-dataset-347107a643b8" target="_blank">Document AI | Document Understanding model at line level with LiLT, Tesseract and DocLayNet dataset</a></li><li>(01/31/2023) <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://medium.com/@pierre_guillou/document-ai-doclaynet-image-viewer-app-3ac54c19956" target="_blank">Document AI | DocLayNet image viewer APP</a></li><li>(01/27/2023) <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://medium.com/@pierre_guillou/document-ai-processing-of-doclaynet-dataset-to-be-used-by-layout-models-of-the-hugging-face-hub-308d8bd81cdb" target="_blank">Document AI | Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference)</a></li></ul></div> 
     """)