Update app.py

app.py

@@ -3,18 +3,59 @@ os.system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/w
 
 from deepdoctection.dataflow import DataFromList
 from deepdoctection import get_dd_analyzer
-from deepdoctection import Image
+import deepdoctection as dd
 import gradio as gr
 
+_DD_ONE = "deepdoctection/configs/conf_dd_one.yaml"
+_TESSERACT = "deepdoctection/configs/conf_tesseract.yaml"
+
+dd.ModelCatalog.register("layout/model_final_inf_only.pt",dd.ModelProfile(
+            name="layout/model_final_inf_only.pt",
+            description="Detectron2 layout detection model trained on private datasets",
+            config="dd/d2/layout/CASCADE_RCNN_R_50_FPN_GN.yaml",
+            size=[274632215],
+            tp_model=False,
+            hf_repo_id=os.environ.get("HF_REPO"),
+            hf_model_name="model_final_inf_only.pt",
+            hf_config_file=["Base-RCNN-FPN.yaml", "CASCADE_RCNN_R_50_FPN_GN.yaml"],
+            categories={"1": dd.names.C.TEXT,
+                        "2": dd.names.C.TITLE,
+                        "3": dd.names.C.LIST,
+                        "4": dd.names.C.TAB,
+                        "5": dd.names.C.FIG},
+        ))
+        
+def get_space_dd_analyzer():
+    # get a dd analyzer with a special layout model
+    lib, device = _auto_select_lib_and_device()
+    dd_one_config_path = _maybe_copy_config_to_cache(_DD_ONE)
+    _maybe_copy_config_to_cache(_TESSERACT)
+
+    # Set up of the configuration and logging
+    cfg = set_config_by_yaml(dd_one_config_path)
+
+    cfg.freeze(freezed=False)
+    cfg.LIB = lib
+    cfg.DEVICE = device
+    cfg.TAB = tables
+    cfg.TAB_REF = table_refinement
+    cfg.OCR = ocr
+    cfg.LANG = language
+    cfg.WEIGHTS.D2LAYOUT = "layout/model_final_inf_only.pt"
+    cfg.freeze()
+    
+    return build_analyzer(cfg)
+    
+    
 
 def analyze_image(img):
     # creating an image object and passing to the analyzer by using dataflows
-    image = Image(file_name="input.png", location="")
+    image = dd.Image(file_name="input.png", location="")
     image.image = img[:,:,::-1]
 
-    df = DataFromList(lst=[image])
+    df = dd.DataFromList(lst=[image])
 
-    analyzer = get_dd_analyzer()
+    analyzer = get_space_dd_analyzer()
 
     df = analyzer.analyze(dataset_dataflow=df)
     df.reset_state()