Create app.py

app.py

@@ -0,0 +1,22 @@
+import streamlit as st
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
+
+# Load model and tokenizer
+model_name = "t5_history_qa"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+qa_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
+
+# Streamlit app
+st.title("History QA with T5 Model")
+st.write("Enter the historical context and your question below:")
+
+context = st.text_area("Context", height=200)
+question = st.text_input("Question")
+
+if st.button("Get Answer"):
+    input_text = f"question: {question} context: {context}"
+    result = qa_pipeline(input_text)
+    answer = result[0]['generated_text']
+    st.write("**Answer:**")
+    st.write(answer)