Update app.py

app.py

@@ -27,7 +27,7 @@ dataset = dataset.shuffle()
 dataset = dataset.select(range(10))
 
 
-def paraphrase_answer(question, answer):
+def paraphrase_answer(question, answer, use_pretrained=False):
     # Combine question and context
     input_text = f"question: {question}. Paraphrase the answer to make it more natural answer: {answer}"
 
@@ -42,7 +42,10 @@ def paraphrase_answer(question, answer):
 
     # Generate the answer
     with torch.no_grad():
-        generated_ids = paraphrase_model.generate(input_ids=input_ids, max_new_tokens=max_target_length)
+        if use_pretrained:
+            generated_ids = pretrained_model.generate(input_ids=input_ids, max_new_tokens=max_target_length)
+        else:
+            generated_ids = paraphrase_model.generate(input_ids=input_ids, max_new_tokens=max_target_length)
 
     # Decode and return the generated answer
     paraphrased_answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
@@ -51,7 +54,7 @@ def paraphrase_answer(question, answer):
 
 
 # Define your function to generate answers
-def generate_answer(question, context, ground_truth, do_pretrained, do_natural):
+def generate_answer(question, context, ground_truth, do_pretrained, do_natural, do_pretrained_natural):
     # Combine question and context
     input_text = f"question: {question} context: {context}"
 
@@ -83,7 +86,12 @@ def generate_answer(question, context, ground_truth, do_pretrained, do_natural):
             pretrained_generated_ids = pretrained_model.generate(input_ids=input_ids, max_new_tokens=max_target_length)
             pretrained_answer = tokenizer.decode(pretrained_generated_ids[0], skip_special_tokens=True)
 
-    return generated_answer, paraphrased_answer, pretrained_answer
+    # Get pretrained model's natural answer
+    pretrained_paraphrased_answer = ""
+    if do_pretrained_natural:
+        pretrained_paraphrased_answer = paraphrase_answer(question, generated_answer, True)
+
+    return generated_answer, paraphrased_answer, pretrained_answer, pretrained_paraphrased_answer
 
 
 # Define a function to list examples from the dataset
@@ -104,13 +112,15 @@ iface = gr.Interface(
         Textbox(label="Question"),
         Textbox(label="Context"),
         Textbox(label="Ground truth"),
-        Checkbox(label="Include pretrained model's result"),
-        Checkbox(label="Include natural answer")
+        Checkbox(label="Include pretrained model's answer"),
+        Checkbox(label="Include natural answer"),
+        Checkbox(label="Include pretrained model's natural answer")
     ],
     outputs=[
         Textbox(label="Generated Answer"),
         Textbox(label="Natural Answer"),
         Textbox(label="Pretrained Model's Answer"),
+        Textbox(label="Pretrained Model's Natural Answer")
     ],
     examples=list_examples()
 )