Update app.py

app.py

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+import streamlit as st
+from transformers import pipeline, BartTokenizer, BartForConditionalGeneration
+
+# Replace with your Hugging Face model repository path for QnA
+model_repo_path_qna = 'ASaboor/Bart_Therapy'
+
+# Load the model and tokenizer for QnA
+model_qna = BartForConditionalGeneration.from_pretrained(model_repo_path_qna)
+tokenizer_qna = BartTokenizer.from_pretrained(model_repo_path_qna)
+
+# Initialize the QnA pipeline
+qna_pipeline = pipeline('question-answering', model=model_qna, tokenizer=tokenizer_qna)
+
+# Streamlit app layout
+st.set_page_config(page_title="QnA App", page_icon=":memo:", layout="wide")
+
+st.title("Question and Answer App")
+st.write("""
+    This app uses a fine-tuned BART model to answer questions based on the provided context.
+    Enter the context and your question below, then click "Get Answer" to see the result.
+""")
+
+# User input for QnA
+context_input = st.text_area("Enter context for QnA", height=300, placeholder="Paste your context here...")
+question_input = st.text_input("Enter question", placeholder="Type your question here...")
+
+# Generate the answer
+if st.button("Get Answer"):
+    if context_input and question_input:
+        with st.spinner("Generating answer..."):
+            try:
+                # Generate answer
+                answer = qna_pipeline({'context': context_input, 'question': question_input})
+                
+                # Display answer
+                st.subheader("Answer")
+                st.write(answer['answer'])
+            except Exception as e:
+                st.error(f"An error occurred during QnA: {e}")
+    else:
+        st.warning("Please enter both context and question for QnA.")
+
+