Update QAPipeline.py

QAPipeline.py

@@ -1,9 +1,4 @@
-# qapipeline.py
-
-from transformers import PreTrainedModel, Pipeline
-from typing import Any, Dict
 from transformers import Pipeline
-from transformers import PreTrainedTokenizer
 from transformers.utils import ModelOutput
 
 from transformers import PreTrainedModel, Pipeline
@@ -13,7 +8,6 @@ class QApipeline(Pipeline):
     def __init__(
         self,
         model: PreTrainedModel,
-        tokenizer: PreTrainedTokenizer,
         **kwargs
     ):
         super().__init__(
@@ -23,30 +17,43 @@ class QApipeline(Pipeline):
 
         print("in __init__")
 
-    def __call__( self, inputs: Dict[str, Any], **kwargs) -> Dict[str, Any]:
-        outputs = self.model.predict(inputs)
+    def __call__( self, question: str, **kwargs) -> Dict[str, Any]:
+        inputs = {
+            "question": question,
+        }
+
+        outputs = self.model.predict(question)
+
         answer = self._process_output(outputs)
+
         print("in __call___")
-        return answer 
+
+        return answer
 
     def _process_output(self, outputs: Any) -> str:
+
         print("in process outputs")
+        print(outputs)
+
         format =  {'guess': outputs[1], 'confidence': outputs[0]}
         return format
-    
+
+
     def _sanitize_parameters(self, **kwargs):
-        print("in sanitize params")
+        print("in sanatize params")
+
         return {}, {}, {}
 
     def preprocess(self, inputs):
         print("in preprocess")
+
         return inputs
 
     def postprocess(self, outputs):
         print("in postprocess")
         format =  {'guess': outputs[1], 'confidence': float(outputs[0])}
         return format
-    
+
     def _forward(self, input_tensors, **forward_parameters: Dict) -> ModelOutput:
         print("in _forward")
-        return super()._forward(input_tensors, **forward_parameters)
+        return super()._forward(input_tensors, **forward_parameters)