Added app.py

app.py

@@ -0,0 +1,39 @@
+import streamlit as st
+import requests
+from transformers import pipeline, BertTokenizer
+
+# Function to generate answers using the BERT model
+def generate_answers(questions, paper_link):
+    # Download the research paper
+    response = requests.get(paper_link)
+    paper_text = response.text
+
+    # Initialize the BERT tokenizer
+    tokenizer = BertTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+
+    # Initialize the question-answering pipeline
+    model = pipeline("question-answering", model="bert-large-uncased-whole-word-masking-finetuned-squad")
+
+    # Generate answers for each question
+    answers = []
+    for question in questions.split(","):
+        inputs = tokenizer(question.strip(), paper_text, return_tensors="pt")
+        answer = model(**inputs)
+        answers.append(answer['answer'])
+
+    return '\n\n'.join(answers)
+
+# Streamlit app
+st.title("Research Paper Question Answering")
+
+questions = st.text_input("Enter comma-separated questions:")
+paper_link = st.text_input("Enter the link to the research paper (Arxiv link):")
+
+if st.button("Generate Answers"):
+    if not (questions and paper_link):
+        st.warning("Please provide both questions and the paper link.")
+    else:
+        with st.spinner("Generating answers..."):
+            answers = generate_answers(questions, paper_link)
+            st.success("Answers generated successfully!")
+            st.text_area("Generated Answers", answers)