fix dependencies

app.py

@@ -15,36 +15,6 @@ def get_model():
 tokenizer, model = get_model()
 
 
-def predict_answer(qa_text_pair):
-    # Encoding
-    encodings = tokenizer(
-        qa_text_pair['question'], qa_text_pair['context'], 
-        max_length=512, 
-        truncation=True,
-        padding="max_length", 
-        return_token_type_ids=False,
-        return_offsets_mapping=True
-    )
-    encodings = {key: torch.tensor([val]).to(device) for key, val in encodings.items()}
-
-    # Predict
-    with torch.no_grad():
-        pred = model(encodings['input_ids'], encodings['attention_mask'])
-        start_logits, end_logits = pred.start_logits, pred.end_logits
-        token_start_index, token_end_index = start_logits.argmax(dim=-1), end_logits.argmax(dim=-1)
-        pred_ids = encodings['input_ids'][0][token_start_index: token_end_index + 1]
-
-        # Answer start/end offset of context.
-        answer_start_offset = int(encodings['offset_mapping'][0][token_start_index][0][0])
-        answer_end_offset = int(encodings['offset_mapping'][0][token_end_index][0][1])
-        answer_offset = (answer_start_offset, answer_end_offset)
-
-    # Decoding
-    answer_text = tokenizer.decode(pred_ids) # text
-    del encodings 
-    return {'answer_text':answer_text, 'answer_offset':answer_offset}
-
-
 ## Title
 st.title('☁️ Bespin → QuestionAnswering')
 
@@ -89,24 +59,26 @@ if st.button("Submit", key='question'):
                               max_length=512, 
                               truncation=True,
                               padding="max_length", 
-                              return_token_type_ids=False
+                              return_token_type_ids=False,
+                              return_offsets_mapping=True
                               )
             encodings = {key: torch.tensor([val]) for key, val in encodings.items()}             
-            input_ids = encodings["input_ids"]
-            attention_mask = encodings["attention_mask"]
 
             # Predict
-            pred = model(input_ids, attention_mask=attention_mask)
-
+            pred = model(encodings["input_ids"], attention_mask=encodings["attention_mask"])
             start_logits, end_logits = pred.start_logits, pred.end_logits
             token_start_index, token_end_index = start_logits.argmax(dim=-1), end_logits.argmax(dim=-1)
-            pred_ids = input_ids[0][token_start_index: token_end_index + 1]
-
-            # Decoding
+            pred_ids = encodings["input_ids"][0][token_start_index: token_end_index + 1]
             prediction = tokenizer.decode(pred_ids)
 
+            # Offset
+            answer_start_offset = int(encodings['offset_mapping'][0][token_start_index][0][0])
+            answer_end_offset = int(encodings['offset_mapping'][0][token_end_index][0][1])
+            answer_offset = (answer_start_offset, answer_end_offset)
+
             # answer
             st.success(prediction)
 
+
     except Exception as e:
         st.error(e)

requirements.txt

@@ -1,3 +1,3 @@
-torch
-transformers==4.16.0
-streamlit
+torch==1.11.0
+transformers==4.20.0
+streamlit==1.10.0