Upload app.py

app.py

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+import streamlit as st
+import os
+from haystack.utils import fetch_archive_from_http, clean_wiki_text, convert_files_to_docs
+from haystack.schema import Answer
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.pipelines import ExtractiveQAPipeline
+from haystack.nodes import FARMReader, TfidfRetriever
+import logging
+from markdown import markdown
+from annotated_text import annotation
+from PIL import Image
+
+os.environ['TOKENIZERS_PARALLELISM'] = "false"
+
+
+# Haystack Components
+@st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None}, allow_output_mutation=True)
+def start_haystack():
+    document_store = InMemoryDocumentStore()
+    load_and_write_data(document_store)
+    retriever = TfidfRetriever(document_store=document_store)
+    reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2-distilled", use_gpu=True)
+    pipeline = ExtractiveQAPipeline(reader, retriever)
+    return pipeline
+
+
+def load_and_write_data(document_store):
+    doc_dir = './amazon_help_docs'
+    docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
+
+    document_store.write_documents(docs)
+
+
+pipeline = start_haystack()
+
+
+def set_state_if_absent(key, value):
+    if key not in st.session_state:
+        st.session_state[key] = value
+
+
+set_state_if_absent("question", "What is amazon music?")
+set_state_if_absent("results", None)
+
+
+def reset_results(*args):
+    st.session_state.results = None
+
+
+# Streamlit App
+
+image = Image.open('got-haystack.png')
+st.image(image)
+
+st.markdown("""
+This QA demo uses a [Haystack Extractive QA Pipeline](https://haystack.deepset.ai/components/ready-made-pipelines#extractiveqapipeline) with 
+an [InMemoryDocumentStore](https://haystack.deepset.ai/components/document-store) which contains documents about Game of Thrones 👑
+Go ahead and ask questions about the marvellous kingdom!
+""", 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!"
+            )
+
+