Update app.py

app.py

@@ -26,13 +26,13 @@ recognitor = Predictor(config)
 #model_name = "microsoft/xdoc-base-squad2.0"
 #nlp = pipeline('question-answering', model=model_name)
 
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
-
-model_name = "timpal0l/mdeberta-v3-base-squad2"
-model = AutoModelForQuestionAnswering.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
+#from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
 
+#model_name = "timpal0l/mdeberta-v3-base-squad2"
+#model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+#tokenizer = AutoTokenizer.from_pretrained(model_name)
+#nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
+'''
 def query(doc, labels):
     questions = labels.split(", ")
     result={}
@@ -47,7 +47,7 @@ def query(doc, labels):
         value = res['answer']
         result[question]=value
     return result
-    
+'''    
 def draw_boxes(image, bounds, color='yellow', width=2):
     draw = ImageDraw.Draw(image)
     for bound in bounds:
@@ -88,8 +88,9 @@ def inference(filepath, lang, labels):
         out = recognitor.predict(cropped_image)
         
         texts = texts + '\t' + out
-    result = query(texts, labels)
-    jsonText = json.dumps(result)
+    #result = query(texts, labels)
+    #jsonText = json.dumps(result)
+    jsonText='{"result":"ok"'
     im = PIL.Image.open(filepath)
     draw_boxes(im, bounds)
     im.save('result.jpg')