Update app.py

app.py

@@ -5,21 +5,7 @@ from transformers import TFAutoModelForQuestionAnswering
 from datasets import Dataset
 import streamlit as st
 
-#prompts
-st.title("Tweet Sentiment Extractor...")
-
-# take text/tweet input
-textbox = st.text_area('Write your text in this box:', '',height=100,  max_chars=500 )
-option = st.selectbox(
-     'How would you like to be contacted?',
-         ('positive', 'negative', 'neutral'))
-                  
-
-python_dict = {"text":textbox, "sentiment":option}
 
-dataset = Dataset.from_dict(python_dict)
-
-MAX_LENGTH = 105
 
 
 # loading saved roberta-base tokenizer to tokenize the text into input IDs that model can make sense of.
@@ -35,83 +21,104 @@ def load_model():
     return TFAutoModelForQuestionAnswering.from_pretrained(model_checkpoint)
 model = load_model()
 
-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
-)
-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
+#prompts
+st.title("Tweet Sentiment Extractor...")
 
-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()
+# take text/tweet input
+textbox = st.text_area('Write your text in this box:', '',height=100,  max_chars=500 )
+option = st.selectbox(
+     'How would you like to be contacted?',
+         ('positive', 'negative', 'neutral'))
+                  
+
+python_dict = {"text":textbox, "sentiment":option}
 
-          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)
-     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"] = X["text"]
-    
-final_data = Dataset.from_pandas(processed_raw_df)
-final_data = final_data.map(predict_answers,batched=True)
-    
-  
+dataset = Dataset.from_dict(python_dict)
+
+MAX_LENGTH = 105
 
-st.markdown("## " +final_data["predicted_answer"]  )
+button = st.button('Extract text of the given sentiment..')
+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
+          )
+          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)
+               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"] = X["text"]
+              
+          final_data = Dataset.from_pandas(processed_raw_df)
+          final_data = final_data.map(predict_answers,batched=True)
+         
+       
+     
+     st.markdown("## " +final_data["predicted_answer"]  )