Update app.py

app.py

@@ -1,21 +1,28 @@
-import transformers
 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')
+        # Close the file before reading its contents
+        temp_file.close()
+        with open(temp_file.name, 'r', encoding='utf-8') as file:
+            document_text = file.read()
         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."
@@ -23,4 +30,4 @@ if bouton_ok:
     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)
+    st.text(summary)