Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+
+# Load the pre-trained model and tokenizer
+model_name = "facebook/bart-base-squad2"
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def answer_query(question, context):
+  # Preprocess the question and context using the tokenizer
+  inputs = tokenizer(question, context, return_tensors="pt")
+
+  # Use the model to get the answer
+  with torch.no_grad():
+    outputs = model(**inputs)
+    start_scores, end_scores = outputs.start_logits, outputs.end_scores
+
+  # Find the most likely answer span
+  answer_start = torch.argmax(start_scores)
+  answer_end = torch.argmax(end_scores) + 1
+
+  # Extract the answer from the context
+  answer = tokenizer.convert_tokens_to_string(context)[answer_start:answer_end]
+
+  return answer
+
+# Streamlit app
+st.title("Question Answering App")
+
+# Textbox for user query
+user_query = st.text_input("Enter your question:")
+
+# File uploader for context
+uploaded_file = st.file_uploader("Upload a context file (txt):")
+
+if uploaded_file is not None:
+  # Read the uploaded file content
+  context = uploaded_file.read().decode("utf-8")
+else:
+  # Use default context if no file uploaded
+  context = "This is a sample context for demonstration purposes. You can upload your own text file for context."
+
+# Answer the query if a question is provided
+if user_query:
+  answer = answer_query(user_query, context)
+  st.write(f"Answer: {answer}")
+else:
+  st.write("Please enter a question.")