'

app.py

@@ -1,7 +1,7 @@
 import json
 import streamlit as st
-# from haystack.document_stores import FAISSDocumentStore
-from haystack.document_stores import InMemoryDocumentStore
+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
@@ -9,22 +9,24 @@ from haystack.pipelines import ExtractiveQAPipeline
 
 st.title("DPR on Supreme Court Judgements (Capital Gain)")
 
-with open("responses.json", 'r') as f:
-  data = json.load(f)
+# 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
-]
+# 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)
+# document_store = InMemoryDocumentStore()
+# document_store.write_documents(documents)
+
+document_store = FAISSDocumentStore.load("faiss_index")
 
 retriever = DensePassageRetriever(
     document_store=document_store,
@@ -32,28 +34,30 @@ retriever = DensePassageRetriever(
     passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
 )
 
-document_store.update_embeddings(retriever)
-# document_store.save("faiss_index")
+# document_store.update_embeddings(retriever)
+# document_store.save("./document_store")
+
+
 
 reader = FARMReader(model_name_or_path="deepset/bert-base-cased-squad2")
 
 pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever)
 
-query = st.text_input("Enter your query:", "")
+# query = st.text_input("Enter your query:", "")
 
-if query:
-    with st.spinner("Searching..."):
-        results = pipeline.run(query=query, params={"Retriever": {"top_k": 5}})
-        st.write("Results:")
-        for idx, result in enumerate(results["documents"]):
-            st.write(f"**{idx + 1}. {result.meta['name']}**")
-            st.write(f"URL: {result.meta['url']}")
-            st.write(result.content)
-            st.write("---")
+# # if query:
+# #     with st.spinner("Searching..."):
+# #         results = pipeline.run(query=query, params={"Retriever": {"top_k": 5}})
+# #         st.write("Results:")
+# #         for idx, result in enumerate(results["documents"]):
+# #             st.write(f"**{idx + 1}. {result.meta['name']}**")
+# #             st.write(f"URL: {result.meta['url']}")
+# #             st.write(result.content)
+# #             st.write("---")
 
 # query = st.text_input("Enter Question")
-# # 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}})
+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}")
+for answer in result['answers']:
+    st.markdown(f"=====================\nAnswer: {answer.answer}\nContext: {answer.context}\nScore: {answer.score}")

faiss_index.json

@@ -0,0 +1 @@
+{"embedding_dim": 768, "faiss_index_factory_str": "Flat"}