App Added

app.py

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+import json
+import streamlit as st
+from haystack.document_stores import FAISSDocumentStore
+from haystack.document_stores import InMemoryDocumentStore
+from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
+from haystack.nodes import DensePassageRetriever
+from haystack.nodes import FARMReader
+from haystack.pipelines import ExtractiveQAPipeline
+
+st.header("DPR on Supreme Court Judgements (Capital Gain)")
+
+with open("responses.json", 'r') as f:
+  data = json.load(f)
+
+documents = [
+    {
+        "content": doc["text"],
+        "meta": {
+            "name": doc["title"],
+            "url": doc["url"]
+        }
+    } for doc in data
+]
+
+document_store = FAISSDocumentStore(embedding_dim=768, faiss_index_factory_str="Flat")
+# document_store = InMemoryDocumentStore()
+document_store.write_documents(documents)
+
+retriever = DensePassageRetriever(
+    document_store=document_store,
+    query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
+    passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
+)
+
+document_store.update_embeddings(retriever)
+document_store.save("faiss_index")
+
+reader = FARMReader(model_name_or_path="deepset/bert-base-cased-squad2")
+
+pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever)
+
+query = st.input()
+# query = "What is the subject matter of the petition in the Sadanand S. Varde case?"
+result = pipeline.run(query=query, params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}})
+
+for answer in result['answers']:
+    st.markdown(f"=====================\nAnswer: {answer.answer}\nContext: {answer.context}\nScore: {answer.score}")

requirements.txt

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+streamlit
+faiss-cpu
+transformers
+sentence-transformers
+farm-haystack
+farm-haystack[faiss]
+farm-haystack[inference]