Update app.py

app.py

@@ -29,40 +29,41 @@ def predict_label(text):
 
 if __name__ == '__main__':
     
-    space_key = os.environ.get('key')
-    filenames = ['network.py', 'layers.py', 'utils.py',
-                 'representation.py', 'predict.py', 'validate.py']
+    # space_key = os.environ.get('key')
+    # filenames = ['network.py', 'layers.py', 'utils.py',
+    #              'representation.py', 'predict.py', 'validate.py']
     
-    for file in filenames:
-        hf_hub_download('nehalelkaref/stagedNER',
-                    filename=file,
-                    local_dir='src',
-                    token=space_key)
+    # for file in filenames:
+    #     hf_hub_download('nehalelkaref/stagedNER',
+    #                 filename=file,
+    #                 local_dir='src',
+    #                 token=space_key)
         
-    CATALOGUE.download_package("all",
-                           recursive=True,
-                           force=True,
-                           print_status=True)
+    # CATALOGUE.download_package("all",
+    #                        recursive=True,
+    #                        force=True,
+    #                        print_status=True)
     
-    from src.predict import extract_spannet_scores,extract_ent_scores,pool_span_scores,pool_ent_scores
-    from src.network import SpanNet, EntNet
-    from src.validate import entities_from_token_classes
+    # from src.predict import extract_spannet_scores,extract_ent_scores,pool_span_scores,pool_ent_scores
+    # from src.network import SpanNet, EntNet
+    # from src.validate import entities_from_token_classes
 
 
-    diasmbig = BERTUnfactoredDisambiguator.pretrained('msa')
-    tagger = DefaultTagger(diasmbig, 'pos')
+    # diasmbig = BERTUnfactoredDisambiguator.pretrained('msa')
+    # tagger = DefaultTagger(diasmbig, 'pos')
     
-    span_path = 'models/span.model'
-    msa_span_path = 'new_models/msa.best.model'
-    entity_path= 'models/entity.msa.model'
+    # span_path = 'models/span.model'
+    # msa_span_path = 'new_models/msa.best.model'
+    # entity_path= 'models/entity.msa.model'
     
-    span_model = SpanNet.load_model(span_path)
-    msa_span_model = SpanNet.load_model(msa_span_path)
-    entity_model = EntNet.load_model(entity_path)
+    # span_model = SpanNet.load_model(span_path)
+    # msa_span_model = SpanNet.load_model(msa_span_path)
+    # entity_model = EntNet.load_model(entity_path)
     # iface= gr.Base(primary_hue="green")
+    comp = gr.Textbox(label="Input Example", lines=1)
     example = gr.Examples(
-      ["النشرة الإخبارية الصادرة عن الأونروا رقم 113 (1986/1/8).",
-      inputs= gr.Textbox(label="Input Example", lines=1)])
+      examples=[["النشرة الإخبارية الصادرة عن الأونروا رقم 113 (1986/1/8)."]],
+      inputs= comp)
     iface = gr.Interface(fn=predict_label, inputs="text", outputs="text",
                          examples=example,theme="finlaymacklon/smooth_slate")
     iface.launch(show_api=False)