Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+import torch
+
+# Load the tokenizer and model
+model_name = 'roberta-base'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForQuestionAnswering.from_pretrained('./results')  # Path to your fine-tuned model
+
+# Set the title for the Streamlit app
+st.title("Movie Trivia Question Answering")
+
+# Text inputs for the user
+context = st.text_area("Enter the context (movie-related text):")
+question = st.text_area("Enter your question:")
+
+def get_answer(context, question):
+    inputs = tokenizer.encode_plus(question, context, return_tensors='pt', truncation=True, padding=True)
+    input_ids = inputs['input_ids'].tolist()[0]
+
+    # Get the model's answer
+    outputs = model(**inputs)
+    answer_start_scores = outputs.start_logits
+    answer_end_scores = outputs.end_logits
+
+    # Get the most likely beginning and end of the answer span
+    answer_start = torch.argmax(answer_start_scores)
+    answer_end = torch.argmax(answer_end_scores) + 1
+
+    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
+    return answer
+
+if st.button("Get Answer"):
+    if context and question:
+        answer = get_answer(context, question)
+        st.subheader("Answer")
+        st.write(answer)
+    else:
+        st.warning("Please enter both context and question.")
+
+# Optionally, add instructions or information about the app
+st.write("""
+    Enter a movie-related context and a question related to the context above. The model will provide the answer based on the context provided.
+    """)