Using post method

app.py

@@ -9,6 +9,8 @@ from collections import namedtuple
 from Nested.utils.helpers import load_checkpoint
 from Nested.utils.data import get_dataloaders, text2segments
 import json
+from pydantic import BaseModel
+from fastapi.responses import JSONResponse
 
 app = FastAPI()
 print("Version 2...")
@@ -52,49 +54,106 @@ label_vocab = label_vocab[0]  # the list loaded from pickle
 id2label = {i: s for i, s in enumerate(label_vocab.itos)}
 
 
-sentence = "ذهب احمد الى السوق"
+class NERRequest(BaseModel):
+    text: str
+
+
+@app.post("/predict")
+def predict(request: NERRequest):
+
+    sentence = request.text  # 👈 user input
+
+    # Load tagger
+    tagger, tag_vocab, train_config = load_checkpoint(checkpoint_path)
+
+    dataset, token_vocab = text2segments(sentence)
+
+    vocabs = namedtuple("Vocab", ["tags", "tokens"])
+    vocab = vocabs(tokens=token_vocab, tags=tag_vocab)
+
+    dataloader = get_dataloaders(
+        (dataset,),
+        vocab,
+        args_data,
+        batch_size=32,
+        shuffle=(False,),
+    )[0]
+
+    segments = tagger.infer(dataloader)
+
+    lists = []
+
+    for segment in segments:
+        for token in segment:
+            item = {}
+            item["token"] = token.text
+
+            list_of_tags = [t["tag"] for t in token.pred_tag]
+            list_of_tags = [i for i in list_of_tags if i not in ("O", " ", "")]
+
+            if not list_of_tags:
+                item["tags"] = ["O"]
+            else:
+                item["tags"] = list_of_tags
+
+            lists.append(item)
+
+    content = {
+        "resp": lists,
+        "statusText": "OK",
+        "statusCode": 0,
+    }
+
+    return JSONResponse(
+        content=content,
+        media_type="application/json",
+        status_code=200,
+    )
+
+
+# sentence = "ذهب احمد إلى جامعة"
 # Load tagger
-tagger, tag_vocab, train_config = load_checkpoint(checkpoint_path)
+# tagger, tag_vocab, train_config = load_checkpoint(checkpoint_path)
 
 # Convert text to a tagger dataset and index the tokens in args.text
-dataset, token_vocab = text2segments(sentence)
+# dataset, token_vocab = text2segments(sentence)
 
-vocabs = namedtuple("Vocab", ["tags", "tokens"])
-vocab = vocabs(tokens=token_vocab, tags=tag_vocab)
+# vocabs = namedtuple("Vocab", ["tags", "tokens"])
+# vocab = vocabs(tokens=token_vocab, tags=tag_vocab)
 
 # From the datasets generate the dataloaders
-dataloader = get_dataloaders(
-    (dataset,),
-    vocab,
-    args_data,
-    batch_size=32,
-    shuffle=(False,),
-)[0]
+# dataloader = get_dataloaders(
+#     (dataset,),
+#     vocab,
+#     args_data,
+#     batch_size=32,
+#     shuffle=(False,),
+# )[0]
 
 # Perform inference on the text and get back the tagged segments
-segments = tagger.infer(dataloader)
-segments_lists = []
+# segments = tagger.infer(dataloader)
+# segments_lists = []
+
+
+## Print results
+## for segment in segments:
+##     s = [
+##         f"{token.text} ({'|'.join([t['tag'] for t in token.pred_tag])})"
+##         for token in segment
+##     ]
+##     print(" ".join(s))
 
 
-# Print results
 # for segment in segments:
-#     s = [
-#         f"{token.text} ({'|'.join([t['tag'] for t in token.pred_tag])})"
-#         for token in segment
-#     ]
-#     print(" ".join(s))
-
-
-for segment in segments:
-    for token in segment:
-        segments_list = {}
-        segments_list["token"] = token.text
-        list_of_tags = [t['tag'] for t in token.pred_tag]
-        list_of_tags = [i for i in list_of_tags if i not in('O',' ','')]
-        if list_of_tags == []:
-            segments_list["tags"] = ' '.join(['O'])
-        else:
-            segments_list["tags"] = ' '.join(list_of_tags)
-        segments_lists.append(segments_list)  
-
-print(segments_lists)        
+#     for token in segment:
+#         segments_list = {}
+#         segments_list["token"] = token.text
+#         list_of_tags = [t['tag'] for t in token.pred_tag]
+#         list_of_tags = [i for i in list_of_tags if i not in('O',' ','')]
+#         if list_of_tags == []:
+#             segments_list["tags"] = ' '.join(['O'])
+#         else:
+#             segments_list["tags"] = ' '.join(list_of_tags)
+#         segments_lists.append(segments_list)  
+
+# print(segments_lists)