Create app.py

app.py

@@ -0,0 +1,57 @@
+import transformers
+from transformers import T5Tokenizer, T5Model, T5ForConditionalGeneration, T5TokenizerFast, TFT5ForConditionalGeneration, FlaxT5ForConditionalGeneration
+import evaluate
+import torch
+import torch.nn as nn
+import pandas as pd
+import gradio as gr
+import requests
+
+Q_LEN = 256
+
+model_name = 'PRAli22/t5-base-question-answering-system'
+tokenizer = T5TokenizerFast.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+def predict_answer(context, question, ref_answer=None):
+    inputs = tokenizer(question, context, max_length=Q_LEN, padding="max_length", truncation=True, add_special_tokens=True)
+
+    input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).unsqueeze(0)
+    attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).unsqueeze(0)
+
+    outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask)
+
+    predicted_answer = tokenizer.decode(outputs.flatten(), skip_special_tokens=True)
+
+    if ref_answer:
+        # Load the Bleu metric
+        bleu = evaluate.load("google_bleu")
+        score = bleu.compute(predictions=[predicted_answer],
+                            references=[ref_answer])
+
+        print("Context: \n", context)
+        print("\n")
+        print("Question: \n", question)
+        return {
+            "Reference Answer: ": ref_answer,
+            "Predicted Answer: ": predicted_answer,
+            "BLEU Score: ": score
+        }
+    else:
+        return predicted_answer
+
+css_code='body{background-image:url("https://media.istockphoto.com/id/1256252051/vector/people-using-online-translation-app.jpg?s=612x612&w=0&k=20&c=aa6ykHXnSwqKu31fFR6r6Y1bYMS5FMAU9yHqwwylA94=");}'
+
+demo = gr.Interface(
+    fn=predict_answer, 
+    inputs=[
+        gr.Textbox(label="text", placeholder="Enter the text "),
+        gr.Textbox(label="question", placeholder="Enter the question")
+    ], 
+    outputs=gr.Textbox(label="answer"), 
+    title="Question Answering System",
+    description= "This is Question Answering System, it takes a text and question in English as inputs and returns it's answer",
+    css = css_code
+)
+
+demo.launch(share=True)