Update app.py

app.py

@@ -2,21 +2,24 @@ import streamlit as st
 from transformers import pipeline
 
 # Define the path to the saved model
-model_path = './QAModel'  
+model_path = './QAModel'  # Path to your fine-tuned model
 
 # Load the question-answering pipeline
 qa_pipeline = pipeline("question-answering", model=model_path, tokenizer=model_path)
 
-# Load the context from a file
-context_file = 'contexts.txt' 
-
-with open(context_file, 'r', encoding='utf-8') as f:
-    default_context = f.read()
-
 # Set the title for the Streamlit app
 st.title("Movie Trivia Question Answering")
 
-# Text input for the user question
+# Load the context from a predefined file
+context_file_path = '/contexts.txt'  # Replace with the actual path to your context file
+with open(context_file_path, 'r') as file:
+    context = file.read()
+
+# Display the context to the user
+st.subheader("Context (movie-related text)")
+st.write(context)
+
+# Text input for the user to enter the question
 question = st.text_area("Enter your question:")
 
 def generate_answer(question, context):
@@ -26,7 +29,7 @@ def generate_answer(question, context):
 
 if st.button("Get Answer"):
     if question:
-        generated_answer = generate_answer(question, default_context)
+        generated_answer = generate_answer(question, context)
         # Display the generated answer
         st.subheader("Answer")
         st.write(generated_answer)
@@ -35,5 +38,5 @@ if st.button("Get Answer"):
 
 # Optionally, add instructions or information about the app
 st.write("""
-    Enter a question related to the provided movie-related context above. The model will provide the answer based on the context provided.
-    """)
+    Enter a question related to the provided movie-related context above. The model will provide the answer based on the context.
+    """)