Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import re
+from collections import Counter
+
+@st.cache_resource
+def load_qa_pipeline():
+    return pipeline("question-answering", model="tareeb23/Roberta_SQUAD_V2")
+
+def normalize_answer(s):
+    """Lower text and remove punctuation, articles and extra whitespace."""
+    def remove_articles(text):
+        return re.sub(r'\b(a|an|the)\b', ' ', text)
+
+    def white_space_fix(text):
+        return ' '.join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return ''.join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+def compute_exact_match(prediction, ground_truth):
+    return int(normalize_answer(prediction) == normalize_answer(ground_truth))
+
+def compute_f1(prediction, ground_truth):
+    prediction_tokens = normalize_answer(prediction).split()
+    ground_truth_tokens = normalize_answer(ground_truth).split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+def main():
+    st.title("Question Answering with RoBERTa")
+
+    # Load the QA pipeline
+    qa_pipeline = load_qa_pipeline()
+
+    # User input for context
+    context = st.text_area("Enter the context:", height=200)
+
+    # User input for question
+    question = st.text_input("Enter your question:")
+
+    if st.button("Get Answer"):
+        if context and question:
+            # Get the answer
+            result = qa_pipeline(question=question, context=context)
+            
+            # Display the result
+            st.subheader("Answer:")
+            st.write(result['answer'])
+            st.write(f"Confidence: {result['score']:.2f}")
+
+            # Store the result for later use
+            st.session_state.last_answer = result['answer']
+
+            # Show option to calculate scores
+            st.subheader("Calculate Scores")
+            if st.checkbox("Show score calculation"):
+                actual_answer = st.text_input("Enter the actual answer:")
+                if st.button("Calculate Scores"):
+                    if actual_answer:
+                        em_score = compute_exact_match(result['answer'], actual_answer)
+                        f1_score = compute_f1(result['answer'], actual_answer)
+                        st.subheader("Scores:")
+                        st.write(f"Exact Match: {em_score}")
+                        st.write(f"F1 Score: {f1_score:.4f}")
+                    else:
+                        st.warning("Please enter the actual answer.")
+        else:
+            st.warning("Please provide both context and question.")
+
+if __name__ == "__main__":
+    main()