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app.py

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+import streamlit as st
+import pandas as pd
+
+from haystack.schema import Answer
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.pipeline import FAQPipeline
+from haystack.retriever.dense import EmbeddingRetriever
+import logging
+
+#Haystack function calls - streamlit structure from Tuana GoT QA Haystack demo
+@st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True) # use streamlit cache
+
+def start_haystack():
+    document_store = InMemoryDocumentStore(index="document", embedding_field='embedding', embedding_dim=384, similarity='cosine')
+    retriever = EmbeddingRetriever(document_store=document_store, embedding_model='sentence-transformers/all-MiniLM-L6-v2', use_gpu=False, top_k=1)
+    load_data_to_store(document_store,retriever)
+    pipeline = FAQPipeline(retriever=retriever)
+    return pipeline
+
+def load_data_to_store(document_store, retriever):
+    df = pd.read_csv('monopoly_qa-v1.csv')
+    questions = list(df.Question)
+    df['embedding'] = retriever.embed_queries(texts=questions)
+    df = df.rename(columns={"Question":"content","Answer":"answer"})
+    df.drop('link to source (to prevent duplicate sources)',axis=1, inplace=True)
+    
+    dicts = df.to_dict(orient="records")
+    document_store.write_documents(dicts)
+ 
+ pipeline = start_haystack()
+ 
+ # Streamlit App section
+ 
+def set_state_if_absent(key, value):
+    if key not in st.session_state:
+        st.session_state[key] = value
+
+set_state_if_absent("question", "how much money should each player have at the beginning?")
+set_state_if_absent("results", None)
+ 
+ 
+ st.markdown( """
+Haystack FAQ Semantic Search Pipeline
+""", unsafe_allow_html=True)
+
+question = st.text_input("", value=st.session_state.question, max_chars=100, on_change=reset_results)
+
+def ask_question(question):
+    prediction = pipeline.run(query=question, params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}})
+    results = []
+    for answer in prediction["answers"]:
+        answer = answer.to_dict()
+        if answer["answer"]:
+            results.append(
+                {
+                    "context": "..." + answer["context"] + "...",
+                    "answer": answer["answer"],
+                    "relevance": round(answer["score"] * 100, 2),
+                    "offset_start_in_doc": answer["offsets_in_document"][0]["start"],
+                }
+            )
+        else:
+            results.append(
+                {
+                    "context": None,
+                    "answer": None,
+                    "relevance": round(answer["score"] * 100, 2),
+                }
+            )
+    return results
+
+if question:
+    with st.spinner("👑    Performing semantic search on royal scripts..."):
+        try:
+            msg = 'Asked ' + question
+            logging.info(msg)
+            st.session_state.results = ask_question(question)    
+        except Exception as e:
+            logging.exception(e)
+    
+
+
+if st.session_state.results:
+    st.write('## Top Results')
+    for count, result in enumerate(st.session_state.results):
+        if result["answer"]:
+            answer, context = result["answer"], result["context"]
+            start_idx = context.find(answer)
+            end_idx = start_idx + len(answer)
+            st.write(
+                markdown(context[:start_idx] + str(annotation(body=answer, label="ANSWER", background="#964448", color='#ffffff')) + context[end_idx:]),
+                unsafe_allow_html=True,
+            )
+            st.markdown(f"**Relevance:** {result['relevance']}")
+        else:
+            st.info(
+                "🤔    Haystack is unsure whether any of the documents contain an answer to your question. Try to reformulate it!"
+            )