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app.py

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+import gradio as gr
+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
+from haystack.utils import print_answers
+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=True, 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()
+ 
+def predict(question):
+  predictions = pipeline.run(question)
+  answer =  predictions["answers"]
+  return answer
+ 
+ gr.Interface(
+    predict,
+    inputs=gr.inputs.Textbox(label="enter your monopoly question here"),
+    outputs=gr.outputs.Label(num_top_classes=1),
+    title="Monopoly FAQ Semantic Search",
+).launch()