Create app.py

Adding a code in app.py to prepare UI and use Google's Bert language model for generating answers of asked questions on research paper content.

app.py

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+import gradio as gr
+import tensorflow as tf
+from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
+
+
+class ResearchPaperQAModel:
+    """Class to load the model and answer questions based on abstract and text of reserach paper.
+    """
+    def __init__(self, model_name):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)
+
+    def answer_question(self, question, abstract, paper_text):
+        # Tokenize input question and context
+        if not paper_text:
+            context = abstract
+        else:
+            context = paper_text
+            
+        inputs = self.tokenizer(question, context, return_tensors="tf")
+
+        # Get the start and end logits for the answer
+        outputs = self.model(**inputs)
+        start_logits, end_logits = outputs.start_logits[0].numpy(), outputs.end_logits[0].numpy()
+
+        # Find the tokens with the highest probability for start and end positions
+        start_index = tf.argmax(start_logits, axis=-1).numpy()
+        end_index = tf.argmax(end_logits, axis=-1).numpy()
+
+        # Convert token indices to actual tokens
+        tokens = self.tokenizer.convert_ids_to_tokens(inputs["input_ids"].numpy().squeeze())
+        answer_tokens = tokens[start_index : end_index + 1]
+
+        # Convert answer tokens back to a string
+        answer = self.tokenizer.convert_tokens_to_string(answer_tokens)
+
+        return answer
+
+
+model = "bert-large-uncased-whole-word-masking-finetuned-squad" # Model name
+paper_model = ResearchPaperQAModel(model) #Create an instance of the model
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=paper_model.answer_question,
+    inputs=["text", "text", "text"],
+    outputs="text",
+    live=True,
+    title="Ask question to research paper",
+    description="Enter title of research paper, abstract, research paper content(optional) and list of questions to get answers."
+)
+
+# Launch the Gradio interface
+iface.launch(share=True)