Update app.py

app.py

@@ -964,20 +964,6 @@ graph = Neo4jGraph(
     password="B_sZbapCTZoQDWj1JrhwqElsNa-jm5Zq1m_mAnyPYog"
 )
 
-# Avoid pushing the graph documents to Neo4j every time
-# Only push the documents once and comment the code below after the initial push
-# dataset_name = "Pijush2023/birmindata07312024"
-# page_content_column = 'events_description'
-# loader = HuggingFaceDatasetLoader(dataset_name, page_content_column)
-# data = loader.load()
-
-# text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=50)
-# documents = text_splitter.split_documents(data)
-
-# llm_transformer = LLMGraphTransformer(llm=chat_model)
-# graph_documents = llm_transformer.convert_to_graph_documents(documents)
-# graph.add_graph_documents(graph_documents, baseEntityLabel=True, include_source=True)
-
 class Entities(BaseModel):
     names: list[str] = Field(..., description="All the person, organization, or business entities that appear in the text")
 
@@ -1029,37 +1015,6 @@ def retriever_neo4j(question: str):
     structured_data = structured_retriever(question)
     return structured_data
 
-_template = """Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question,
-in its original language.
-Chat History:
-{chat_history}
-Follow Up Input: {question}
-Standalone question:"""
-
-CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
-
-def _format_chat_history(chat_history: list[tuple[str, str]]) -> list:
-    buffer = []
-    for human, ai in chat_history:
-        buffer.append(HumanMessage(content=human))
-        buffer.append(AIMessage(content=ai))
-    return buffer
-
-_search_query = RunnableBranch(
-    (
-        RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config(
-            run_name="HasChatHistoryCheck"
-        ),
-        RunnablePassthrough.assign(
-            chat_history=lambda x: _format_chat_history(x["chat_history"])
-        )
-        | CONDENSE_QUESTION_PROMPT
-        | ChatOpenAI(temperature=0, api_key=os.environ['OPENAI_API_KEY'])
-        | StrOutputParser(),
-    ),
-    RunnableLambda(lambda x : x["question"]),
-)
-
 template = """Answer the question based only on the following context:
 {context}
 Question: {question}
@@ -1096,8 +1051,10 @@ def generate_answer(message, choice, retrieval_mode):
         response = qa_chain({"query": message})
         return response['result'], extract_addresses(response['result'])
     elif retrieval_mode == "Knowledge-Graph":
-        response = chain_neo4j.invoke({"question": message})
-        return response, extract_addresses(response)
+        context = retriever_neo4j(message)
+        qa_chain = ChatPromptTemplate.from_template(prompt_template.template)
+        response = qa_chain.invoke({"context": context, "question": message})
+        return response['result'], extract_addresses(response['result'])
     else:
         return "Invalid retrieval mode selected.", []
 
@@ -1298,7 +1255,7 @@ def show_map_if_details(history, choice):
     if choice in ["Details", "Conversational"]:
         return gr.update(visible=True), update_map_with_response(history)
     else:
-        return gr.update(visible=False), ""
+        return gr.update(visible(False)), ""
 
 def generate_audio_elevenlabs(text):
     XI_API_KEY = os.environ['ELEVENLABS_API']
@@ -1681,3 +1638,4 @@ demo.launch(share=True)
 
 
 
+