Create app.py

app.py

@@ -0,0 +1,49 @@
+import gradio as gr
+from simpletransformers.ner import NERModel 
+import string
+labels = ["O",
+"B-FOOD_QUANTITY",
+"B-FOOD_SIZE",
+"B-FOOD",
+"I-FOOD",
+"B-FOOD_INGREDIENTS",
+"I-FOOD_INGREDIENTS",
+"B-DRINK_SIZE",
+"B-DRINK_QUANTITY",
+"B-DRINK",
+"B-PAYMENT",
+"I-PAYMENT",
+"B-DELIVERY_ADDRESS",
+"I-DRINK_SIZE",
+"I-DRINK",
+"I-FOOD_SIZE",
+"I-ING",
+"I-DELIVERY_ADDRESS"]
+model = NERModel(
+        "albert", 
+        "bgk/lodosalberttr", labels=labels,
+        use_cuda=False,
+        ignore_mismatched_sizes=True
+    )
+examples=['I want two hamburgers and one sprite and one milkshake send it to my workplace' , ' I want to order two large pizzas, two medium coke, send it to my home, I will pay with cash' ]
+def ner(text):
+    trans_table = text.maketrans('', '', string.punctuation)
+    text = text.translate(trans_table)
+    text=text.lower()
+    
+    prediction, model_output = model.predict([text])
+    entities = prediction
+    filtered_output = (({v: k} for d in sublist for k, v in d.items() if (v.startswith("B-") or v.startswith("I-"))) for sublist in prediction)
+    entities = []
+    for sublist in filtered_output:
+        for d in sublist:
+            for k, v in d.items():
+                label = k.split("-")[1]
+                entities.extend([(label, v)])
+              
+    return  entities # prediction
+demo = gr.Interface(ner,
+             gr.Textbox(placeholder="Enter sentence here..."),   
+             gr.HighlightedText(),
+             examples=examples)
+demo.launch()