Add paraphrased answer

app.py

@@ -1,23 +1,54 @@
 import gradio as gr
 from gradio.components import Textbox
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, T5ForConditionalGeneration
+from peft import PeftModel, PeftConfig
 import torch
 import datasets
 
 # Load your fine-tuned model and tokenizer
-model_name = "legacy107/flan-t5-large-bottleneck-adapter-cpgQA-unique"
+model_name = "google/flan-t5-large"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
+model.load_adapter("legacy107/adapter-flan-t5-large-bottleneck-adapter-cpgQA", source="hf")
 model.set_active_adapters("question_answering")
+
+peft_name = "legacy107/flan-t5-large-ia3-bioasq-paraphrase"
+peft_config = PeftConfig.from_pretrained(peft_name)
+paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+paraphrase_model = PeftModel.from_pretrained(paraphrase_model, peft_name)
+
 max_length = 512
 max_target_length = 128
 
 # Load your dataset
-dataset = datasets.load_dataset("minh21/cpgQA-v1.0-unique-context-test-10-percent", split="test")
+dataset = datasets.load_dataset("minh21/cpgQA-v1.0-unique-context-test-10-percent-validation-10-percent", split="test")
 dataset = dataset.shuffle()
 dataset = dataset.select(range(5))
 
 
+def paraphrase_answer(question, answer):
+    # Combine question and context
+    input_text = f"question: {question}. Paraphrase the answer to make it more natural answer: {answer}"
+
+    # Tokenize the input text
+    input_ids = tokenizer(
+        input_text,
+        return_tensors="pt",
+        padding="max_length",
+        truncation=True,
+        max_length=max_length,
+    ).input_ids
+
+    # Generate the answer
+    with torch.no_grad():
+        generated_ids = paraphrase_model.generate(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)
+
+    return paraphrased_answer
+
+
 # Define your function to generate answers
 def generate_answer(question, context):
     # Combine question and context
@@ -39,7 +70,10 @@ def generate_answer(question, context):
     # Decode and return the generated answer
     generated_answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
 
-    return generated_answer
+    # Paraphrase answer
+    paraphrased_answer = paraphrase_answer(question, generated_answer)
+
+    return generated_answer, paraphrased_answer
 
 
 # Define a function to list examples from the dataset
@@ -59,7 +93,10 @@ iface = gr.Interface(
         Textbox(label="Question"),
         Textbox(label="Context")
     ],
-    outputs=Textbox(label="Generated Answer"),
+    outputs=[
+        Textbox(label="Generated Answer"),
+        Textbox(label="Natural Answer")
+    ],
     examples=list_examples()
 )