rename vision, add layour and tsr recognizer (#70)

* rename vision, add layour and tsr recognizer

* trivial fixing

api/apps/conversation_app.py

 
 @manager.route('/set', methods=['POST'])
 @login_required
-@validate_request("dialog_id")
 def set_conversation():
     req = request.json
     conv_id = req.get("conversation_id")
 
 @manager.route('/completion', methods=['POST'])
 @login_required
-@validate_request("dialog_id", "messages")
+@validate_request("conversation_id", "messages")
 def completion():
     req = request.json
     msg = []
         if m["role"] == "assistant" and not msg: continue
         msg.append({"role": m["role"], "content": m["content"]})
     try:
-        e, dia = DialogService.get_by_id(req["dialog_id"])
+        e, conv = ConversationService.get_by_id(req["conversation_id"])
+        if not e:
+            return get_data_error_result(retmsg="Conversation not found!")
+        conv.message.append(msg[-1])
+        e, dia = DialogService.get_by_id(conv.dialog_id)
         if not e:
             return get_data_error_result(retmsg="Dialog not found!")
-        del req["dialog_id"]
+        del req["conversation_id"]
         del req["messages"]
-        return get_json_result(data=chat(dia, msg, **req))
+        ans = chat(dia, msg, **req)
+        conv.reference.append(ans["reference"])
+        conv.message.append({"role": "assistant", "content": ans["answer"]})
+        ConversationService.update_by_id(conv.id, conv.to_dict())
+        return get_json_result(data=ans)
     except Exception as e:
         return server_error_response(e)
 
                                     dialog.vector_similarity_weight, top=1024, aggs=False)
     knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
 
-    if not knowledges and prompt_config["empty_response"]:
-        return {"answer": prompt_config["empty_response"], "retrieval": kbinfos}
+    if not knowledges and prompt_config.get("empty_response"):
+        return {"answer": prompt_config["empty_response"], "reference": kbinfos}
 
     kwargs["knowledge"] = "\n".join(knowledges)
     gen_conf = dialog.llm_setting
         gen_conf["max_tokens"] = min(gen_conf["max_tokens"], llm.max_tokens - used_token_count)
     answer = chat_mdl.chat(prompt_config["system"].format(**kwargs), msg, gen_conf)
 
-    answer = retrievaler.insert_citations(answer,
+    if knowledges:
+        answer = retrievaler.insert_citations(answer,
                                           [ck["content_ltks"] for ck in kbinfos["chunks"]],
                                           [ck["vector"] for ck in kbinfos["chunks"]],
                                           embd_mdl,
                                           vtweight=dialog.vector_similarity_weight)
     for c in kbinfos["chunks"]:
         if c.get("vector"): del c["vector"]
-    return {"answer": answer, "retrieval": kbinfos}
+    return {"answer": answer, "reference": kbinfos}
 
 
 def use_sql(question, field_map, tenant_id, chat_mdl):

api/apps/llm_app.py

     model_type = request.args.get("model_type")
     try:
         objs = TenantLLMService.query(tenant_id=current_user.id)
-        mdlnms = set([o.to_dict()["llm_name"] for o in objs if o.api_key])
+        facts = set([o.to_dict()["llm_factory"] for o in objs if o.api_key])
         llms = LLMService.get_all()
         llms = [m.to_dict() for m in llms if m.status == StatusEnum.VALID.value]
         for m in llms:
-            m["available"] = m["llm_name"] in mdlnms
+            m["available"] = m["fid"] in facts
 
         res = {}
         for m in llms:

api/db/db_models.py

     token_num = IntegerField(default=0)
     chunk_num = IntegerField(default=0)
     progress = FloatField(default=0)
-    progress_msg = CharField(max_length=512, null=True, help_text="process message", default="")
+    progress_msg = CharField(max_length=4096, null=True, help_text="process message", default="")
     process_begin_at = DateTimeField(null=True)
     process_duation = FloatField(default=0)
     run = CharField(max_length=1, null=True, help_text="start to run processing or cancel.(1: run it; 2: cancel)", default="0")
     begin_at = DateTimeField(null=True)
     process_duation = FloatField(default=0)
     progress = FloatField(default=0)
-    progress_msg = CharField(max_length=255, null=True, help_text="process message", default="")
+    progress_msg = CharField(max_length=4096, null=True, help_text="process message", default="")
 
 
 class Dialog(DataBaseModel):
     dialog_id = CharField(max_length=32, null=False, index=True)
     name = CharField(max_length=255, null=True, help_text="converastion name")
     message = JSONField(null=True)
+    reference = JSONField(null=True, default=[])
 
     class Meta:
         db_table = "conversation"

api/db/services/llm_service.py

 
         model_config = cls.get_api_key(tenant_id, mdlnm)
         if not model_config:
-            raise LookupError("Model({}) not found".format(mdlnm))
+            raise LookupError("Model({}) not authorized".format(mdlnm))
         model_config = model_config.to_dict()
         if llm_type == LLMType.EMBEDDING.value:
             if model_config["llm_factory"] not in EmbeddingModel:

deepdoc/vision/__init__.py

+from .ocr import OCR
+from .recognizer import Recognizer
+from .layout_recognizer import LayoutRecognizer
+from .table_structure_recognizer import TableStructureRecognizer

deepdoc/vision/layout_recognizer.py

+import os
+import re
+from collections import Counter
+from copy import deepcopy
+
+import numpy as np
+
+from api.utils.file_utils import get_project_base_directory
+from .recognizer import Recognizer
+
+
+class LayoutRecognizer(Recognizer):
+    def __init__(self, domain):
+        self.layout_labels = [
+             "_background_",
+             "Text",
+             "Title",
+             "Figure",
+             "Figure caption",
+             "Table",
+             "Table caption",
+             "Header",
+             "Footer",
+             "Reference",
+             "Equation",
+        ]
+        super().__init__(self.layout_labels, domain,
+                         os.path.join(get_project_base_directory(), "rag/res/deepdoc/"))
+
+    def __call__(self, image_list, ocr_res, scale_factor=3, thr=0.7, batch_size=16):
+        def __is_garbage(b):
+            patt = [r"^•+$", r"(版权归©|免责条款|地址[:：])", r"\.{3,}", "^[0-9]{1,2} / ?[0-9]{1,2}$",
+                    r"^[0-9]{1,2} of [0-9]{1,2}$", "^http://[^ ]{12,}",
+                    "(资料|数据)来源[:：]", "[0-9a-z._-]+@[a-z0-9-]+\\.[a-z]{2,3}",
+                    "\\(cid *: *[0-9]+ *\\)"
+                    ]
+            return any([re.search(p, b["text"]) for p in patt])
+
+        layouts = super().__call__(image_list, thr, batch_size)
+        # save_results(image_list, layouts, self.layout_labels, output_dir='output/', threshold=0.7)
+        assert len(image_list) == len(ocr_res)
+        # Tag layout type
+        boxes = []
+        assert len(image_list) == len(layouts)
+        garbages = {}
+        page_layout = []
+        for pn, lts in enumerate(layouts):
+            bxs = ocr_res[pn]
+            lts = [{"type": b["type"],
+                    "score": float(b["score"]),
+                    "x0": b["bbox"][0] / scale_factor, "x1": b["bbox"][2] / scale_factor,
+                    "top": b["bbox"][1] / scale_factor, "bottom": b["bbox"][-1] / scale_factor,
+                    "page_number": pn,
+                    } for b in lts]
+            lts = self.sort_Y_firstly(lts, np.mean([l["bottom"]-l["top"] for l in lts]) / 2)
+            lts = self.layouts_cleanup(bxs, lts)
+            page_layout.append(lts)
+
+            # Tag layout type, layouts are ready
+            def findLayout(ty):
+                nonlocal bxs, lts, self
+                lts_ = [lt for lt in lts if lt["type"] == ty]
+                i = 0
+                while i < len(bxs):
+                    if bxs[i].get("layout_type"):
+                        i += 1
+                        continue
+                    if __is_garbage(bxs[i]):
+                        bxs.pop(i)
+                        continue
+
+                    ii = self.find_overlapped_with_threashold(bxs[i], lts_,
+                                                                thr=0.4)
+                    if ii is None:  # belong to nothing
+                        bxs[i]["layout_type"] = ""
+                        i += 1
+                        continue
+                    lts_[ii]["visited"] = True
+                    if lts_[ii]["type"] in ["footer", "header", "reference"]:
+                        if lts_[ii]["type"] not in garbages:
+                            garbages[lts_[ii]["type"]] = []
+                        garbages[lts_[ii]["type"]].append(bxs[i]["text"])
+                        bxs.pop(i)
+                        continue
+
+                    bxs[i]["layoutno"] = f"{ty}-{ii}"
+                    bxs[i]["layout_type"] = lts_[ii]["type"]
+                    i += 1
+
+            for lt in ["footer", "header", "reference", "figure caption",
+                       "table caption", "title", "text", "table", "figure", "equation"]:
+                findLayout(lt)
+
+            # add box to figure layouts which has not text box
+            for i, lt in enumerate(
+                    [lt for lt in lts if lt["type"] == "figure"]):
+                if lt.get("visited"):
+                    continue
+                lt = deepcopy(lt)
+                del lt["type"]
+                lt["text"] = ""
+                lt["layout_type"] = "figure"
+                lt["layoutno"] = f"figure-{i}"
+                bxs.append(lt)
+
+            boxes.extend(bxs)
+
+        ocr_res = boxes
+
+        garbag_set = set()
+        for k in garbages.keys():
+            garbages[k] = Counter(garbages[k])
+            for g, c in garbages[k].items():
+                if c > 1:
+                    garbag_set.add(g)
+
+        ocr_res = [b for b in ocr_res if b["text"].strip() not in garbag_set]
+        return ocr_res, page_layout
+

deepdoc/visual/ocr.py → deepdoc/vision/ocr.py

         self.rec_batch_num = 16
         postprocess_params = {
             'name': 'CTCLabelDecode',
-            "character_dict_path": os.path.join(get_project_base_directory(), "rag/res", "ocr.res"),
+            "character_dict_path": os.path.join(os.path.dirname(os.path.realpath(__file__)), "ocr.res"),
             "use_space_char": True
         }
         self.postprocess_op = build_post_process(postprocess_params)
 
         """
         if not model_dir:
-            model_dir = snapshot_download(repo_id="InfiniFlow/ocr")
+            model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc")
 
         self.text_detector = TextDetector(model_dir)
         self.text_recognizer = TextRecognizer(model_dir)

deepdoc/vision/recognizer.py

+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+import os
+from copy import deepcopy
+
+import onnxruntime as ort
+from huggingface_hub import snapshot_download
+
+from . import seeit
+from .operators import *
+from rag.settings import cron_logger
+
+
+class Recognizer(object):
+    def __init__(self, label_list, task_name, model_dir=None):
+        """
+        If you have trouble downloading HuggingFace models, -_^ this might help!!
+
+        For Linux:
+        export HF_ENDPOINT=https://hf-mirror.com
+
+        For Windows:
+        Good luck
+        ^_-
+
+        """
+        if not model_dir:
+            model_dir = snapshot_download(repo_id="InfiniFlow/ocr")
+
+        model_file_path = os.path.join(model_dir, task_name + ".onnx")
+        if not os.path.exists(model_file_path):
+            raise ValueError("not find model file path {}".format(
+                model_file_path))
+        if ort.get_device() == "GPU":
+            self.ort_sess = ort.InferenceSession(model_file_path, providers=['CUDAExecutionProvider'])
+        else:
+            self.ort_sess = ort.InferenceSession(model_file_path, providers=['CPUExecutionProvider'])
+        self.label_list = label_list
+
+    @staticmethod
+    def sort_Y_firstly(arr, threashold):
+        # sort using y1 first and then x1
+        arr = sorted(arr, key=lambda r: (r["top"], r["x0"]))
+        for i in range(len(arr) - 1):
+            for j in range(i, -1, -1):
+                # restore the order using th
+                if abs(arr[j + 1]["top"] - arr[j]["top"]) < threashold \
+                        and arr[j + 1]["x0"] < arr[j]["x0"]:
+                    tmp = deepcopy(arr[j])
+                    arr[j] = deepcopy(arr[j + 1])
+                    arr[j + 1] = deepcopy(tmp)
+        return arr
+
+    @staticmethod
+    def sort_X_firstly(arr, threashold, copy=True):
+        # sort using y1 first and then x1
+        arr = sorted(arr, key=lambda r: (r["x0"], r["top"]))
+        for i in range(len(arr) - 1):
+            for j in range(i, -1, -1):
+                # restore the order using th
+                if abs(arr[j + 1]["x0"] - arr[j]["x0"]) < threashold \
+                        and arr[j + 1]["top"] < arr[j]["top"]:
+                    tmp = deepcopy(arr[j]) if copy else arr[j]
+                    arr[j] = deepcopy(arr[j + 1]) if copy else arr[j + 1]
+                    arr[j + 1] = deepcopy(tmp) if copy else tmp
+        return arr
+
+    @staticmethod
+    def sort_C_firstly(arr, thr=0):
+        # sort using y1 first and then x1
+        # sorted(arr, key=lambda r: (r["x0"], r["top"]))
+        arr = Recognizer.sort_X_firstly(arr, thr)
+        for i in range(len(arr) - 1):
+            for j in range(i, -1, -1):
+                # restore the order using th
+                if "C" not in arr[j] or "C" not in arr[j + 1]:
+                    continue
+                if arr[j + 1]["C"] < arr[j]["C"] \
+                        or (
+                        arr[j + 1]["C"] == arr[j]["C"]
+                        and arr[j + 1]["top"] < arr[j]["top"]
+                ):
+                    tmp = arr[j]
+                    arr[j] = arr[j + 1]
+                    arr[j + 1] = tmp
+        return arr
+
+        return sorted(arr, key=lambda r: (r.get("C", r["x0"]), r["top"]))
+
+    @staticmethod
+    def sort_R_firstly(arr, thr=0):
+        # sort using y1 first and then x1
+        # sorted(arr, key=lambda r: (r["top"], r["x0"]))
+        arr = Recognizer.sort_Y_firstly(arr, thr)
+        for i in range(len(arr) - 1):
+            for j in range(i, -1, -1):
+                if "R" not in arr[j] or "R" not in arr[j + 1]:
+                    continue
+                if arr[j + 1]["R"] < arr[j]["R"] \
+                        or (
+                        arr[j + 1]["R"] == arr[j]["R"]
+                        and arr[j + 1]["x0"] < arr[j]["x0"]
+                ):
+                    tmp = arr[j]
+                    arr[j] = arr[j + 1]
+                    arr[j + 1] = tmp
+        return arr
+
+    @staticmethod
+    def overlapped_area(a, b, ratio=True):
+        tp, btm, x0, x1 = a["top"], a["bottom"], a["x0"], a["x1"]
+        if b["x0"] > x1 or b["x1"] < x0:
+            return 0
+        if b["bottom"] < tp or b["top"] > btm:
+            return 0
+        x0_ = max(b["x0"], x0)
+        x1_ = min(b["x1"], x1)
+        assert x0_ <= x1_, "Fuckedup! T:{},B:{},X0:{},X1:{} ==> {}".format(
+            tp, btm, x0, x1, b)
+        tp_ = max(b["top"], tp)
+        btm_ = min(b["bottom"], btm)
+        assert tp_ <= btm_, "Fuckedup! T:{},B:{},X0:{},X1:{} => {}".format(
+            tp, btm, x0, x1, b)
+        ov = (btm_ - tp_) * (x1_ - x0_) if x1 - \
+                                           x0 != 0 and btm - tp != 0 else 0
+        if ov > 0 and ratio:
+            ov /= (x1 - x0) * (btm - tp)
+        return ov
+
+    @staticmethod
+    def layouts_cleanup(boxes, layouts, far=2, thr=0.7):
+        def notOverlapped(a, b):
+            return any([a["x1"] < b["x0"],
+                        a["x0"] > b["x1"],
+                        a["bottom"] < b["top"],
+                        a["top"] > b["bottom"]])
+
+        i = 0
+        while i + 1 < len(layouts):
+            j = i + 1
+            while j < min(i + far, len(layouts)) \
+                    and (layouts[i].get("type", "") != layouts[j].get("type", "")
+                         or notOverlapped(layouts[i], layouts[j])):
+                j += 1
+            if j >= min(i + far, len(layouts)):
+                i += 1
+                continue
+            if Recognizer.overlapped_area(layouts[i], layouts[j]) < thr \
+                    and Recognizer.overlapped_area(layouts[j], layouts[i]) < thr:
+                i += 1
+                continue
+
+            if layouts[i].get("score") and layouts[j].get("score"):
+                if layouts[i]["score"] > layouts[j]["score"]:
+                    layouts.pop(j)
+                else:
+                    layouts.pop(i)
+                continue
+
+            area_i, area_i_1 = 0, 0
+            for b in boxes:
+                if not notOverlapped(b, layouts[i]):
+                    area_i += Recognizer.overlapped_area(b, layouts[i], False)
+                if not notOverlapped(b, layouts[j]):
+                    area_i_1 += Recognizer.overlapped_area(b, layouts[j], False)
+
+            if area_i > area_i_1:
+                layouts.pop(j)
+            else:
+                layouts.pop(i)
+
+        return layouts
+
+    def create_inputs(self, imgs, im_info):
+        """generate input for different model type
+        Args:
+            imgs (list(numpy)): list of images (np.ndarray)
+            im_info (list(dict)): list of image info
+        Returns:
+            inputs (dict): input of model
+        """
+        inputs = {}
+
+        im_shape = []
+        scale_factor = []
+        if len(imgs) == 1:
+            inputs['image'] = np.array((imgs[0],)).astype('float32')
+            inputs['im_shape'] = np.array(
+                (im_info[0]['im_shape'],)).astype('float32')
+            inputs['scale_factor'] = np.array(
+                (im_info[0]['scale_factor'],)).astype('float32')
+            return inputs
+
+        for e in im_info:
+            im_shape.append(np.array((e['im_shape'],)).astype('float32'))
+            scale_factor.append(np.array((e['scale_factor'],)).astype('float32'))
+
+        inputs['im_shape'] = np.concatenate(im_shape, axis=0)
+        inputs['scale_factor'] = np.concatenate(scale_factor, axis=0)
+
+        imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
+        max_shape_h = max([e[0] for e in imgs_shape])
+        max_shape_w = max([e[1] for e in imgs_shape])
+        padding_imgs = []
+        for img in imgs:
+            im_c, im_h, im_w = img.shape[:]
+            padding_im = np.zeros(
+                (im_c, max_shape_h, max_shape_w), dtype=np.float32)
+            padding_im[:, :im_h, :im_w] = img
+            padding_imgs.append(padding_im)
+        inputs['image'] = np.stack(padding_imgs, axis=0)
+        return inputs
+
+    @staticmethod
+    def find_overlapped(box, boxes_sorted_by_y, naive=False):
+        if not boxes_sorted_by_y:
+            return
+        bxs = boxes_sorted_by_y
+        s, e, ii = 0, len(bxs), 0
+        while s < e and not naive:
+            ii = (e + s) // 2
+            pv = bxs[ii]
+            if box["bottom"] < pv["top"]:
+                e = ii
+                continue
+            if box["top"] > pv["bottom"]:
+                s = ii + 1
+                continue
+            break
+        while s < ii:
+            if box["top"] > bxs[s]["bottom"]:
+                s += 1
+            break
+        while e - 1 > ii:
+            if box["bottom"] < bxs[e - 1]["top"]:
+                e -= 1
+            break
+
+        max_overlaped_i, max_overlaped = None, 0
+        for i in range(s, e):
+            ov = Recognizer.overlapped_area(bxs[i], box)
+            if ov <= max_overlaped:
+                continue
+            max_overlaped_i = i
+            max_overlaped = ov
+
+        return max_overlaped_i
+
+    @staticmethod
+    def find_overlapped_with_threashold(box, boxes, thr=0.3):
+        if not boxes:
+            return
+        max_overlaped_i, max_overlaped, _max_overlaped = None, thr, 0
+        s, e = 0, len(boxes)
+        for i in range(s, e):
+            ov = Recognizer.overlapped_area(box, boxes[i])
+            _ov = Recognizer.overlapped_area(boxes[i], box)
+            if (ov, _ov) < (max_overlaped, _max_overlaped):
+                continue
+            max_overlaped_i = i
+            max_overlaped = ov
+            _max_overlaped = _ov
+
+        return max_overlaped_i
+
+    def preprocess(self, image_list):
+        preprocess_ops = []
+        for op_info in [
+            {'interp': 2, 'keep_ratio': False, 'target_size': [800, 608], 'type': 'LinearResize'},
+            {'is_scale': True, 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'type': 'StandardizeImage'},
+            {'type': 'Permute'},
+            {'stride': 32, 'type': 'PadStride'}
+        ]:
+            new_op_info = op_info.copy()
+            op_type = new_op_info.pop('type')
+            preprocess_ops.append(eval(op_type)(**new_op_info))
+
+        inputs = []
+        for im_path in image_list:
+            im, im_info = preprocess(im_path, preprocess_ops)
+            inputs.append({"image": np.array((im,)).astype('float32'), "scale_factor": np.array((im_info["scale_factor"],)).astype('float32')})
+        return inputs
+
+    def __call__(self, image_list, thr=0.7, batch_size=16):
+        res = []
+        imgs = []
+        for i in range(len(image_list)):
+            if not isinstance(image_list[i], np.ndarray):
+                imgs.append(np.array(image_list[i]))
+            else: imgs.append(image_list[i])
+
+        batch_loop_cnt = math.ceil(float(len(imgs)) / batch_size)
+        for i in range(batch_loop_cnt):
+            start_index = i * batch_size
+            end_index = min((i + 1) * batch_size, len(imgs))
+            batch_image_list = imgs[start_index:end_index]
+            inputs = self.preprocess(batch_image_list)
+            for ins in inputs:
+                bb = []
+                for b in self.ort_sess.run(None, ins)[0]:
+                    clsid, bbox, score = int(b[0]), b[2:], b[1]
+                    if score < thr:
+                        continue
+                    if clsid >= len(self.label_list):
+                        cron_logger.warning(f"bad category id")
+                        continue
+                    bb.append({
+                        "type": self.label_list[clsid].lower(),
+                        "bbox": [float(t) for t in bbox.tolist()],
+                        "score": float(score)
+                    })
+                res.append(bb)
+
+        #seeit.save_results(image_list, res, self.label_list, threshold=thr)
+
+        return res

deepdoc/vision/table_structure_recognizer.py

+import logging
+import os
+import re
+from collections import Counter
+from copy import deepcopy
+
+import numpy as np
+
+from api.utils.file_utils import get_project_base_directory
+from rag.nlp import huqie
+from .recognizer import Recognizer
+
+
+class TableStructureRecognizer(Recognizer):
+    def __init__(self):
+        self.labels = [
+            "table",
+            "table column",
+            "table row",
+            "table column header",
+            "table projected row header",
+            "table spanning cell",
+        ]
+        super().__init__(self.labels, "tsr",
+                         os.path.join(get_project_base_directory(), "rag/res/deepdoc/"))
+
+    def __call__(self, images, thr=0.5):
+        tbls = super().__call__(images, thr)
+        res = []
+        # align left&right for rows, align top&bottom for columns
+        for tbl in tbls:
+            lts = [{"label": b["type"],
+                    "score": b["score"],
+                    "x0": b["bbox"][0], "x1": b["bbox"][2],
+                    "top": b["bbox"][1], "bottom": b["bbox"][-1]
+                    } for b in tbl]
+            if not lts:
+                continue
+
+            left = [b["x0"] for b in lts if b["label"].find(
+                "row") > 0 or b["label"].find("header") > 0]
+            right = [b["x1"] for b in lts if b["label"].find(
+                "row") > 0 or b["label"].find("header") > 0]
+            if not left:
+                continue
+            left = np.median(left) if len(left) > 4 else np.min(left)
+            right = np.median(right) if len(right) > 4 else np.max(right)
+            for b in lts:
+                if b["label"].find("row") > 0 or b["label"].find("header") > 0:
+                    if b["x0"] > left:
+                        b["x0"] = left
+                    if b["x1"] < right:
+                        b["x1"] = right
+
+            top = [b["top"] for b in lts if b["label"] == "table column"]
+            bottom = [b["bottom"] for b in lts if b["label"] == "table column"]
+            if not top:
+                res.append(lts)
+                continue
+            top = np.median(top) if len(top) > 4 else np.min(top)
+            bottom = np.median(bottom) if len(bottom) > 4 else np.max(bottom)
+            for b in lts:
+                if b["label"] == "table column":
+                    if b["top"] > top:
+                        b["top"] = top
+                    if b["bottom"] < bottom:
+                        b["bottom"] = bottom
+
+            res.append(lts)
+        return res
+
+    @staticmethod
+    def is_caption(bx):
+        patt = [
+            r"[图表]+[ 0-9:：]{2,}"
+        ]
+        if any([re.match(p, bx["text"].strip()) for p in patt]) \
+                or bx["layout_type"].find("caption") >= 0:
+            return True
+        return False
+
+    def __blockType(self, b):
+        patt = [
+            ("^(20|19)[0-9]{2}[年/-][0-9]{1,2}[月/-][0-9]{1,2}日*$", "Dt"),
+            (r"^(20|19)[0-9]{2}年$", "Dt"),
+            (r"^(20|19)[0-9]{2}[年-][0-9]{1,2}月*$", "Dt"),
+            ("^[0-9]{1,2}[月-][0-9]{1,2}日*$", "Dt"),
+            (r"^第*[一二三四1-4]季度$", "Dt"),
+            (r"^(20|19)[0-9]{2}年*[一二三四1-4]季度$", "Dt"),
+            (r"^(20|19)[0-9]{2}[ABCDE]$", "Dt"),
+            ("^[0-9.,+%/ -]+$", "Nu"),
+            (r"^[0-9A-Z/\._~-]+$", "Ca"),
+            (r"^[A-Z]*[a-z' -]+$", "En"),
+            (r"^[0-9.,+-]+[0-9A-Za-z/$￥%<>（）()' -]+$", "NE"),
+            (r"^.{1}$", "Sg")
+        ]
+        for p, n in patt:
+            if re.search(p, b["text"].strip()):
+                return n
+        tks = [t for t in huqie.qie(b["text"]).split(" ") if len(t) > 1]
+        if len(tks) > 3:
+            if len(tks) < 12:
+                return "Tx"
+            else:
+                return "Lx"
+
+        if len(tks) == 1 and huqie.tag(tks[0]) == "nr":
+            return "Nr"
+
+        return "Ot"
+
+    def construct_table(self, boxes, is_english=False, html=False):
+        cap = ""
+        i = 0
+        while i < len(boxes):
+            if self.is_caption(boxes[i]):
+                cap += boxes[i]["text"]
+                boxes.pop(i)
+                i -= 1
+            i += 1
+
+        if not boxes:
+            return []
+        for b in boxes:
+            b["btype"] = self.__blockType(b)
+        max_type = Counter([b["btype"] for b in boxes]).items()
+        max_type = max(max_type, key=lambda x: x[1])[0] if max_type else ""
+        logging.debug("MAXTYPE: " + max_type)
+
+        rowh = [b["R_bott"] - b["R_top"] for b in boxes if "R" in b]
+        rowh = np.min(rowh) if rowh else 0
+        boxes = self.sort_R_firstly(boxes, rowh / 2)
+        boxes[0]["rn"] = 0
+        rows = [[boxes[0]]]
+        btm = boxes[0]["bottom"]
+        for b in boxes[1:]:
+            b["rn"] = len(rows) - 1
+            lst_r = rows[-1]
+            if lst_r[-1].get("R", "") != b.get("R", "") \
+                    or (b["top"] >= btm - 3 and lst_r[-1].get("R", "-1") != b.get("R", "-2")
+            ):  # new row
+                btm = b["bottom"]
+                b["rn"] += 1
+                rows.append([b])
+                continue
+            btm = (btm + b["bottom"]) / 2.
+            rows[-1].append(b)
+
+        colwm = [b["C_right"] - b["C_left"] for b in boxes if "C" in b]
+        colwm = np.min(colwm) if colwm else 0
+        crosspage = len(set([b["page_number"] for b in boxes])) > 1
+        if crosspage:
+            boxes = self.sort_X_firstly(boxes, colwm / 2, False)
+        else:
+            boxes = self.sort_C_firstly(boxes, colwm / 2)
+        boxes[0]["cn"] = 0
+        cols = [[boxes[0]]]
+        right = boxes[0]["x1"]
+        for b in boxes[1:]:
+            b["cn"] = len(cols) - 1
+            lst_c = cols[-1]
+            if (int(b.get("C", "1")) - int(lst_c[-1].get("C", "1")) == 1 and b["page_number"] == lst_c[-1][
+                "page_number"]) \
+                    or (b["x0"] >= right and lst_c[-1].get("C", "-1") != b.get("C", "-2")):  # new col
+                right = b["x1"]
+                b["cn"] += 1
+                cols.append([b])
+                continue
+            right = (right + b["x1"]) / 2.
+            cols[-1].append(b)
+
+        tbl = [[[] for _ in range(len(cols))] for _ in range(len(rows))]
+        for b in boxes:
+            tbl[b["rn"]][b["cn"]].append(b)
+
+        if len(rows) >= 4:
+            # remove single in column
+            j = 0
+            while j < len(tbl[0]):
+                e, ii = 0, 0
+                for i in range(len(tbl)):
+                    if tbl[i][j]:
+                        e += 1
+                        ii = i
+                    if e > 1:
+                        break
+                if e > 1:
+                    j += 1
+                    continue
+                f = (j > 0 and tbl[ii][j - 1] and tbl[ii]
+                [j - 1][0].get("text")) or j == 0
+                ff = (j + 1 < len(tbl[ii]) and tbl[ii][j + 1] and tbl[ii]
+                [j + 1][0].get("text")) or j + 1 >= len(tbl[ii])
+                if f and ff:
+                    j += 1
+                    continue
+                bx = tbl[ii][j][0]
+                logging.debug("Relocate column single: " + bx["text"])
+                # j column only has one value
+                left, right = 100000, 100000
+                if j > 0 and not f:
+                    for i in range(len(tbl)):
+                        if tbl[i][j - 1]:
+                            left = min(left, np.min(
+                                [bx["x0"] - a["x1"] for a in tbl[i][j - 1]]))
+                if j + 1 < len(tbl[0]) and not ff:
+                    for i in range(len(tbl)):
+                        if tbl[i][j + 1]:
+                            right = min(right, np.min(
+                                [a["x0"] - bx["x1"] for a in tbl[i][j + 1]]))
+                assert left < 100000 or right < 100000
+                if left < right:
+                    for jj in range(j, len(tbl[0])):
+                        for i in range(len(tbl)):
+                            for a in tbl[i][jj]:
+                                a["cn"] -= 1
+                    if tbl[ii][j - 1]:
+                        tbl[ii][j - 1].extend(tbl[ii][j])
+                    else:
+                        tbl[ii][j - 1] = tbl[ii][j]
+                    for i in range(len(tbl)):
+                        tbl[i].pop(j)
+
+                else:
+                    for jj in range(j + 1, len(tbl[0])):
+                        for i in range(len(tbl)):
+                            for a in tbl[i][jj]:
+                                a["cn"] -= 1
+                    if tbl[ii][j + 1]:
+                        tbl[ii][j + 1].extend(tbl[ii][j])
+                    else:
+                        tbl[ii][j + 1] = tbl[ii][j]
+                    for i in range(len(tbl)):
+                        tbl[i].pop(j)
+                cols.pop(j)
+        assert len(cols) == len(tbl[0]), "Column NO. miss matched: %d vs %d" % (
+            len(cols), len(tbl[0]))
+
+        if len(cols) >= 4:
+            # remove single in row
+            i = 0
+            while i < len(tbl):
+                e, jj = 0, 0
+                for j in range(len(tbl[i])):
+                    if tbl[i][j]:
+                        e += 1
+                        jj = j
+                    if e > 1:
+                        break
+                if e > 1:
+                    i += 1
+                    continue
+                f = (i > 0 and tbl[i - 1][jj] and tbl[i - 1]
+                [jj][0].get("text")) or i == 0
+                ff = (i + 1 < len(tbl) and tbl[i + 1][jj] and tbl[i + 1]
+                [jj][0].get("text")) or i + 1 >= len(tbl)
+                if f and ff:
+                    i += 1
+                    continue
+
+                bx = tbl[i][jj][0]
+                logging.debug("Relocate row single: " + bx["text"])
+                # i row only has one value
+                up, down = 100000, 100000
+                if i > 0 and not f:
+                    for j in range(len(tbl[i - 1])):
+                        if tbl[i - 1][j]:
+                            up = min(up, np.min(
+                                [bx["top"] - a["bottom"] for a in tbl[i - 1][j]]))
+                if i + 1 < len(tbl) and not ff:
+                    for j in range(len(tbl[i + 1])):
+                        if tbl[i + 1][j]:
+                            down = min(down, np.min(
+                                [a["top"] - bx["bottom"] for a in tbl[i + 1][j]]))
+                assert up < 100000 or down < 100000
+                if up < down:
+                    for ii in range(i, len(tbl)):
+                        for j in range(len(tbl[ii])):
+                            for a in tbl[ii][j]:
+                                a["rn"] -= 1
+                    if tbl[i - 1][jj]:
+                        tbl[i - 1][jj].extend(tbl[i][jj])
+                    else:
+                        tbl[i - 1][jj] = tbl[i][jj]
+                    tbl.pop(i)
+
+                else:
+                    for ii in range(i + 1, len(tbl)):
+                        for j in range(len(tbl[ii])):
+                            for a in tbl[ii][j]:
+                                a["rn"] -= 1
+                    if tbl[i + 1][jj]:
+                        tbl[i + 1][jj].extend(tbl[i][jj])
+                    else:
+                        tbl[i + 1][jj] = tbl[i][jj]
+                    tbl.pop(i)
+                rows.pop(i)
+
+        # which rows are headers
+        hdset = set([])
+        for i in range(len(tbl)):
+            cnt, h = 0, 0
+            for j, arr in enumerate(tbl[i]):
+                if not arr:
+                    continue
+                cnt += 1
+                if max_type == "Nu" and arr[0]["btype"] == "Nu":
+                    continue
+                if any([a.get("H") for a in arr]) \
+                        or (max_type == "Nu" and arr[0]["btype"] != "Nu"):
+                    h += 1
+            if h / cnt > 0.5:
+                hdset.add(i)
+
+        if html:
+            return [self.__html_table(cap, hdset,
+                                      self.__cal_spans(boxes, rows,
+                                                       cols, tbl, True)
+                                      )]
+
+        return self.__desc_table(cap, hdset,
+                                 self.__cal_spans(boxes, rows, cols, tbl, False),
+                                 is_english)
+
+    def __html_table(self, cap, hdset, tbl):
+        # constrcut HTML
+        html = "<table>"
+        if cap:
+            html += f"<caption>{cap}</caption>"
+        for i in range(len(tbl)):
+            row = "<tr>"
+            txts = []
+            for j, arr in enumerate(tbl[i]):
+                if arr is None:
+                    continue
+                if not arr:
+                    row += "<td></td>" if i not in hdset else "<th></th>"
+                    continue
+                txt = ""
+                if arr:
+                    h = min(np.min([c["bottom"] - c["top"] for c in arr]) / 2, 10)
+                    txt = "".join([c["text"]
+                                   for c in self.sort_Y_firstly(arr, h)])
+                txts.append(txt)
+                sp = ""
+                if arr[0].get("colspan"):
+                    sp = "colspan={}".format(arr[0]["colspan"])
+                if arr[0].get("rowspan"):
+                    sp += " rowspan={}".format(arr[0]["rowspan"])
+                if i in hdset:
+                    row += f"<th {sp} >" + txt + "</th>"
+                else:
+                    row += f"<td {sp} >" + txt + "</td>"
+
+            if i in hdset:
+                if all([t in hdset for t in txts]):
+                    continue
+                for t in txts:
+                    hdset.add(t)
+
+            if row != "<tr>":
+                row += "</tr>"
+            else:
+                row = ""
+            html += "\n" + row
+        html += "\n</table>"
+        return html
+
+    def __desc_table(self, cap, hdr_rowno, tbl, is_english):
+        # get text of every colomn in header row to become header text
+        clmno = len(tbl[0])
+        rowno = len(tbl)
+        headers = {}
+        hdrset = set()
+        lst_hdr = []
+        de = "的" if not is_english else " for "
+        for r in sorted(list(hdr_rowno)):
+            headers[r] = ["" for _ in range(clmno)]
+            for i in range(clmno):
+                if not tbl[r][i]:
+                    continue
+                txt = "".join([a["text"].strip() for a in tbl[r][i]])
+                headers[r][i] = txt
+                hdrset.add(txt)
+            if all([not t for t in headers[r]]):
+                del headers[r]
+                hdr_rowno.remove(r)
+                continue
+            for j in range(clmno):
+                if headers[r][j]:
+                    continue
+                if j >= len(lst_hdr):
+                    break
+                headers[r][j] = lst_hdr[j]
+            lst_hdr = headers[r]
+        for i in range(rowno):
+            if i not in hdr_rowno:
+                continue
+            for j in range(i + 1, rowno):
+                if j not in hdr_rowno:
+                    break
+                for k in range(clmno):
+                    if not headers[j - 1][k]:
+                        continue
+                    if headers[j][k].find(headers[j - 1][k]) >= 0:
+                        continue
+                    if len(headers[j][k]) > len(headers[j - 1][k]):
+                        headers[j][k] += (de if headers[j][k]
+                                          else "") + headers[j - 1][k]
+                    else:
+                        headers[j][k] = headers[j - 1][k] \
+                                        + (de if headers[j - 1][k] else "") \
+                                        + headers[j][k]
+
+        logging.debug(
+            f">>>>>>>>>>>>>>>>>{cap}：SIZE:{rowno}X{clmno} Header: {hdr_rowno}")
+        row_txt = []
+        for i in range(rowno):
+            if i in hdr_rowno:
+                continue
+            rtxt = []
+
+            def append(delimer):
+                nonlocal rtxt, row_txt
+                rtxt = delimer.join(rtxt)
+                if row_txt and len(row_txt[-1]) + len(rtxt) < 64:
+                    row_txt[-1] += "\n" + rtxt
+                else:
+                    row_txt.append(rtxt)
+
+            r = 0
+            if len(headers.items()):
+                _arr = [(i - r, r) for r, _ in headers.items() if r < i]
+                if _arr:
+                    _, r = min(_arr, key=lambda x: x[0])
+
+            if r not in headers and clmno <= 2:
+                for j in range(clmno):
+                    if not tbl[i][j]:
+                        continue
+                    txt = "".join([a["text"].strip() for a in tbl[i][j]])
+                    if txt:
+                        rtxt.append(txt)
+                if rtxt:
+                    append("：")
+                continue
+
+            for j in range(clmno):
+                if not tbl[i][j]:
+                    continue
+                txt = "".join([a["text"].strip() for a in tbl[i][j]])
+                if not txt:
+                    continue
+                ctt = headers[r][j] if r in headers else ""
+                if ctt:
+                    ctt += "："
+                ctt += txt
+                if ctt:
+                    rtxt.append(ctt)
+
+            if rtxt:
+                row_txt.append("; ".join(rtxt))
+
+        if cap:
+            if is_english:
+                from_ = " in "
+            else:
+                from_ = "来自"
+            row_txt = [t + f"\t——{from_}“{cap}”" for t in row_txt]
+        return row_txt
+
+    def __cal_spans(self, boxes, rows, cols, tbl, html=True):
+        # caculate span
+        clft = [np.mean([c.get("C_left", c["x0"]) for c in cln])
+                for cln in cols]
+        crgt = [np.mean([c.get("C_right", c["x1"]) for c in cln])
+                for cln in cols]
+        rtop = [np.mean([c.get("R_top", c["top"]) for c in row])
+                for row in rows]
+        rbtm = [np.mean([c.get("R_btm", c["bottom"])
+                         for c in row]) for row in rows]
+        for b in boxes:
+            if "SP" not in b:
+                continue
+            b["colspan"] = [b["cn"]]
+            b["rowspan"] = [b["rn"]]
+            # col span
+            for j in range(0, len(clft)):
+                if j == b["cn"]:
+                    continue
+                if clft[j] + (crgt[j] - clft[j]) / 2 < b["H_left"]:
+                    continue
+                if crgt[j] - (crgt[j] - clft[j]) / 2 > b["H_right"]:
+                    continue
+                b["colspan"].append(j)
+            # row span
+            for j in range(0, len(rtop)):
+                if j == b["rn"]:
+                    continue
+                if rtop[j] + (rbtm[j] - rtop[j]) / 2 < b["H_top"]:
+                    continue
+                if rbtm[j] - (rbtm[j] - rtop[j]) / 2 > b["H_bott"]:
+                    continue
+                b["rowspan"].append(j)
+
+        def join(arr):
+            if not arr:
+                return ""
+            return "".join([t["text"] for t in arr])
+
+        # rm the spaning cells
+        for i in range(len(tbl)):
+            for j, arr in enumerate(tbl[i]):
+                if not arr:
+                    continue
+                if all(["rowspan" not in a and "colspan" not in a for a in arr]):
+                    continue
+                rowspan, colspan = [], []
+                for a in arr:
+                    if isinstance(a.get("rowspan", 0), list):
+                        rowspan.extend(a["rowspan"])
+                    if isinstance(a.get("colspan", 0), list):
+                        colspan.extend(a["colspan"])
+                rowspan, colspan = set(rowspan), set(colspan)
+                if len(rowspan) < 2 and len(colspan) < 2:
+                    for a in arr:
+                        if "rowspan" in a:
+                            del a["rowspan"]
+                        if "colspan" in a:
+                            del a["colspan"]
+                    continue
+                rowspan, colspan = sorted(rowspan), sorted(colspan)
+                rowspan = list(range(rowspan[0], rowspan[-1] + 1))
+                colspan = list(range(colspan[0], colspan[-1] + 1))
+                assert i in rowspan, rowspan
+                assert j in colspan, colspan
+                arr = []
+                for r in rowspan:
+                    for c in colspan:
+                        arr_txt = join(arr)
+                        if tbl[r][c] and join(tbl[r][c]) != arr_txt:
+                            arr.extend(tbl[r][c])
+                        tbl[r][c] = None if html else arr
+                for a in arr:
+                    if len(rowspan) > 1:
+                        a["rowspan"] = len(rowspan)
+                    elif "rowspan" in a:
+                        del a["rowspan"]
+                    if len(colspan) > 1:
+                        a["colspan"] = len(colspan)
+                    elif "colspan" in a:
+                        del a["colspan"]
+                tbl[rowspan[0]][colspan[0]] = arr
+
+        return tbl
+

deepdoc/visual/__init__.py

-from .ocr import OCR
-from .recognizer import Recognizer

deepdoc/visual/recognizer.py

-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-import os
-import onnxruntime as ort
-from huggingface_hub import snapshot_download
-
-from .operators import *
-from rag.settings import cron_logger
-
-
-class Recognizer(object):
-    def __init__(self, label_list, task_name, model_dir=None):
-        """
-        If you have trouble downloading HuggingFace models, -_^ this might help!!
-
-        For Linux:
-        export HF_ENDPOINT=https://hf-mirror.com
-
-        For Windows:
-        Good luck
-        ^_-
-
-        """
-        if not model_dir:
-            model_dir = snapshot_download(repo_id="InfiniFlow/ocr")
-
-        model_file_path = os.path.join(model_dir, task_name + ".onnx")
-        if not os.path.exists(model_file_path):
-            raise ValueError("not find model file path {}".format(
-                model_file_path))
-        if ort.get_device() == "GPU":
-            self.ort_sess = ort.InferenceSession(model_file_path, providers=['CUDAExecutionProvider'])
-        else:
-            self.ort_sess = ort.InferenceSession(model_file_path, providers=['CPUExecutionProvider'])
-        self.label_list = label_list
-
-    def create_inputs(self, imgs, im_info):
-        """generate input for different model type
-        Args:
-            imgs (list(numpy)): list of images (np.ndarray)
-            im_info (list(dict)): list of image info
-        Returns:
-            inputs (dict): input of model
-        """
-        inputs = {}
-
-        im_shape = []
-        scale_factor = []
-        if len(imgs) == 1:
-            inputs['image'] = np.array((imgs[0],)).astype('float32')
-            inputs['im_shape'] = np.array(
-                (im_info[0]['im_shape'],)).astype('float32')
-            inputs['scale_factor'] = np.array(
-                (im_info[0]['scale_factor'],)).astype('float32')
-            return inputs
-
-        for e in im_info:
-            im_shape.append(np.array((e['im_shape'],)).astype('float32'))
-            scale_factor.append(np.array((e['scale_factor'],)).astype('float32'))
-
-        inputs['im_shape'] = np.concatenate(im_shape, axis=0)
-        inputs['scale_factor'] = np.concatenate(scale_factor, axis=0)
-
-        imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
-        max_shape_h = max([e[0] for e in imgs_shape])
-        max_shape_w = max([e[1] for e in imgs_shape])
-        padding_imgs = []
-        for img in imgs:
-            im_c, im_h, im_w = img.shape[:]
-            padding_im = np.zeros(
-                (im_c, max_shape_h, max_shape_w), dtype=np.float32)
-            padding_im[:, :im_h, :im_w] = img
-            padding_imgs.append(padding_im)
-        inputs['image'] = np.stack(padding_imgs, axis=0)
-        return inputs
-
-    def preprocess(self, image_list):
-        preprocess_ops = []
-        for op_info in [
-            {'interp': 2, 'keep_ratio': False, 'target_size': [800, 608], 'type': 'LinearResize'},
-            {'is_scale': True, 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'type': 'StandardizeImage'},
-            {'type': 'Permute'},
-            {'stride': 32, 'type': 'PadStride'}
-        ]:
-            new_op_info = op_info.copy()
-            op_type = new_op_info.pop('type')
-            preprocess_ops.append(eval(op_type)(**new_op_info))
-
-        inputs = []
-        for im_path in image_list:
-            im, im_info = preprocess(im_path, preprocess_ops)
-            inputs.append({"image": np.array((im,)).astype('float32'), "scale_factor": np.array((im_info["scale_factor"],)).astype('float32')})
-        return inputs
-
-
-    def __call__(self, image_list, thr=0.7, batch_size=16):
-        res = []
-        imgs = []
-        for i in range(len(image_list)):
-            if not isinstance(image_list[i], np.ndarray):
-                imgs.append(np.array(image_list[i]))
-            else: imgs.append(image_list[i])
-
-        batch_loop_cnt = math.ceil(float(len(imgs)) / batch_size)
-        for i in range(batch_loop_cnt):
-            start_index = i * batch_size
-            end_index = min((i + 1) * batch_size, len(imgs))
-            batch_image_list = imgs[start_index:end_index]
-            inputs = self.preprocess(batch_image_list)
-            for ins in inputs:
-                bb = []
-                for b in self.ort_sess.run(None, ins)[0]:
-                    clsid, bbox, score = int(b[0]), b[2:], b[1]
-                    if score < thr:
-                        continue
-                    if clsid >= len(self.label_list):
-                        cron_logger.warning(f"bad category id")
-                        continue
-                    bb.append({
-                        "type": self.label_list[clsid].lower(),
-                        "bbox": [float(t) for t in bbox.tolist()],
-                        "score": float(score)
-                    })
-                res.append(bb)
-
-        #seeit.save_results(image_list, res, self.label_list, threshold=thr)
-
-        return res

rag/svr/task_broker.py

 from api.db.db_models import Task
 from api.db.db_utils import bulk_insert_into_db
 from api.db.services.task_service import TaskService
-from deepdoc.parser import HuParser
+from deepdoc.parser import PdfParser
 from rag.settings import cron_logger
 from rag.utils import MINIO
 from rag.utils import findMaxTm
 
         tsks = []
         if r["type"] == FileType.PDF.value:
-            pages = HuParser.total_page_number(r["name"], MINIO.get(r["kb_id"], r["location"]))
+            pages = PdfParser.total_page_number(r["name"], MINIO.get(r["kb_id"], r["location"]))
             for s,e in r["parser_config"].get("pages", [(0,100000)]):
                 e = min(e, pages)
                 for p in range(s, e, 10):