Update app.py

app.py

@@ -38,86 +38,90 @@ dataset = Dataset.from_dict(python_dict)
 MAX_LENGTH = 105
 
 button = st.button('Click here to extract the word/phrase from the text with the given sentiment: {0}..'.format(option))
-if button:
-     with st.spinner('In progress.......'):
-     
-          def process_data(examples):
-               questions = examples["sentiment"]
-               context = examples["text"]
-               inputs = tokenizer(
-                      questions,
-                      context,
-                      max_length = MAX_LENGTH,
-                      padding="max_length",
-                      return_offsets_mapping = True,   
+
+if not textbox:
+     st.markdown("## " + "Please write your text above!")
+ else:
+     if button:
+          with st.spinner('In progress.......'):
+            
+               def process_data(examples):
+                    questions = examples["sentiment"]
+                    context = examples["text"]
+                    inputs = tokenizer(
+                           questions,
+                           context,
+                           max_length = MAX_LENGTH,
+                           padding="max_length",
+                           return_offsets_mapping = True,   
+                    )
+                    # Assigning None values to all offset mapping of tokens which are not the context tokens.
+                    for i in range(len(inputs["input_ids"])):
+                         offset = inputs["offset_mapping"][i]
+                         sequence_ids = inputs.sequence_ids(i)
+                         inputs["offset_mapping"][i] = [
+                               o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)
+                         ]
+                    return inputs
+                   
+               processed_raw_data = dataset.map(
+                       process_data,
+                       batched = True
                )
-               # Assigning None values to all offset mapping of tokens which are not the context tokens.
-               for i in range(len(inputs["input_ids"])):
-                    offset = inputs["offset_mapping"][i]
-                    sequence_ids = inputs.sequence_ids(i)
-                    inputs["offset_mapping"][i] = [
-                          o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)
-                    ]
-               return inputs
-              
-          processed_raw_data = dataset.map(
-                  process_data,
-                  batched = True
-          )
-          tf_raw_dataset = processed_raw_data.to_tf_dataset(
-                  columns=["input_ids", "attention_mask"],
-                  shuffle=False,
-                  batch_size=1,
-              )
-          
-          # final predictions.
-          outputs = model.predict(tf_raw_dataset)
-          start_logits = outputs.start_logits
-          end_logits = outputs.end_logits
-              
-              # Post Processing.
-              # Using start_logits and end_logits to generate the final answer from the given context.
-          n_best = 20
-          
-          def predict_answers(inputs):
-               predicted_answer = []
-               for i in range(len(inputs["offset_mapping"])):
-                    start_logit = inputs["start_logits"][i]
-                    end_logit = inputs["end_logits"][i]
-                    context = inputs["text"][i]
-                    offset = inputs["offset_mapping"][i]
-                    start_indexes = np.argsort(start_logit)[-1: -n_best - 1:-1].tolist()
-                    end_indexes = np.argsort(end_logit)[-1: -n_best - 1: -1].tolist()
-          
-                    flag = False
-                    for start_index in start_indexes:
-                         for end_index in end_indexes:
-                              # skip answer that are not in the context.
-                              if offset[start_index] is None or offset[end_index] is None:
-                                   continue
-                              # skip answer with length that is either < 0
-                              if end_index < start_index:
-                                   continue
-                              flag = True
-                              answer = context[offset[start_index][0]: offset[end_index][1]]
+               tf_raw_dataset = processed_raw_data.to_tf_dataset(
+                       columns=["input_ids", "attention_mask"],
+                       shuffle=False,
+                       batch_size=1,
+                   )
+               
+               # final predictions.
+               outputs = model.predict(tf_raw_dataset)
+               start_logits = outputs.start_logits
+               end_logits = outputs.end_logits
+                   
+                   # Post Processing.
+                   # Using start_logits and end_logits to generate the final answer from the given context.
+               n_best = 20
+               
+               def predict_answers(inputs):
+                    predicted_answer = []
+                    for i in range(len(inputs["offset_mapping"])):
+                         start_logit = inputs["start_logits"][i]
+                         end_logit = inputs["end_logits"][i]
+                         context = inputs["text"][i]
+                         offset = inputs["offset_mapping"][i]
+                         start_indexes = np.argsort(start_logit)[-1: -n_best - 1:-1].tolist()
+                         end_indexes = np.argsort(end_logit)[-1: -n_best - 1: -1].tolist()
+               
+                         flag = False
+                         for start_index in start_indexes:
+                              for end_index in end_indexes:
+                                   # skip answer that are not in the context.
+                                   if offset[start_index] is None or offset[end_index] is None:
+                                        continue
+                                   # skip answer with length that is either < 0
+                                   if end_index < start_index:
+                                        continue
+                                   flag = True
+                                   answer = context[offset[start_index][0]: offset[end_index][1]]
+                                   predicted_answer.append(answer)
+                                   break
+                              if flag:
+                                   break
+                         if not flag:
                               predicted_answer.append(answer)
-                              break
-                         if flag:
-                              break
-                    if not flag:
-                         predicted_answer.append(answer)
-               return {"predicted_answer":predicted_answer}
-              
-          processed_raw_data.set_format("pandas")
+                    return {"predicted_answer":predicted_answer}
+                   
+               processed_raw_data.set_format("pandas")
+                   
+               processed_raw_df =  processed_raw_data[:]
+               processed_raw_df["start_logits"] = start_logits.tolist()
+               processed_raw_df["end_logits"] = end_logits.tolist()
+               processed_raw_df["text"] = python_dict["text"]
+                   
+               final_data = Dataset.from_pandas(processed_raw_df)
+               final_data = final_data.map(predict_answers,batched=True)
               
-          processed_raw_df =  processed_raw_data[:]
-          processed_raw_df["start_logits"] = start_logits.tolist()
-          processed_raw_df["end_logits"] = end_logits.tolist()
-          processed_raw_df["text"] = python_dict["text"]
-              
-          final_data = Dataset.from_pandas(processed_raw_df)
-          final_data = final_data.map(predict_answers,batched=True)
-         
-       
-     
-     st.markdown("## " +final_data["predicted_answer"][0])
+            
+          
+          st.markdown("## " +final_data["predicted_answer"][0])