Create app.py

app.py

@@ -0,0 +1,30 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import tempfile
+
+# Corrected model class name
+model_name = "potsawee/t5-large-generation-squad-QuestionAnswer"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+uploaded_file = st.file_uploader("Upload Document or Paragraph")
+
+if uploaded_file is not None:
+    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+        temp_file.write(uploaded_file.read())
+        document_text = temp_file.read().decode('utf-8')
+        st.success("Document uploaded successfully!")
+else:
+    document_text = st.text_area("Enter Text (Optional)", height=200)
+
+question = st.text_input("Ask a Question")
+bouton_ok = st.button("Answer")
+
+if bouton_ok:
+    # Improved prompt for better context
+    context = document_text if document_text else "Empty document."
+    inputs = tokenizer.encode(f"Question: {question} Context: {context}", return_tensors='pt', max_length=512, truncation=True)
+    outputs = model.generate(inputs, max_length=150, min_length=80, length_penalty=5, num_beams=2)
+    summary = tokenizer.decode(outputs[0])
+    st.text("Answer:")
+    st.text(summary)