modifed to accept chat history from client

src/app.py

@@ -1,4 +1,4 @@
-
+import json
 import os
 from threading import Lock
 from typing import Any, Dict, Optional, Tuple
@@ -26,8 +26,10 @@ system_template = """
 The context below contains excerpts from 'Let's Talk,' by Andrea A. Lunsford. You must only use the information in the context below to formulate your response. If there is not enough information to formulate  a response, you must respond with
 "I'm sorry, but I can't find the answer to your question in, the book Let's Talk..."
 
-Here is the context:
+Begin context:
 {context}
+End context.
+
 {chat_history}
 """
 
@@ -60,6 +62,44 @@ def getretriever():
 
 retriever = getretriever()
 
+def predict(message):
+  print(message)
+  msgJson = json.loads(message)
+  print(msgJson)
+  messages = [
+    SystemMessagePromptTemplate.from_template(system_template),
+    HumanMessagePromptTemplate.from_template("{question}")
+  ]
+  qa_prompt = ChatPromptTemplate.from_messages(messages)
+
+  llm = ChatOpenAI(
+        openai_api_key=API_KEY,
+        model_name=MODEL,
+        verbose=True)
+  memory = AnswerConversationBufferMemory(memory_key="chat_history", return_messages=True)
+  for msg in msgJson["history"]:
+    memory.save_context({"input": msg[0]}, {"answer": msg[1]})
+
+  chain = ConversationalRetrievalChain.from_llm(
+    llm,
+    retriever=retriever,
+    return_source_documents=USE_VERBOSE,
+    memory=memory,
+    verbose=USE_VERBOSE,
+    combine_docs_chain_kwargs={"prompt": qa_prompt})
+  chain.rephrase_question = False
+  lock = Lock()
+  lock.acquire()
+  try:
+    output = chain({"question": msgJson["question"]})
+    output = output["answer"]
+  except Exception as e:
+    print(e)
+    raise e
+  finally:
+    lock.release()
+  return output
+
 def getanswer(chain, question, history):
   if hasattr(chain, "value"):
     chain = chain.value
@@ -135,4 +175,7 @@ with gr.Blocks() as block:
       ex5 = gr.Button(value="How do I cite a Reddit thread?", variant="primary")
       ex5.click(getanswer, inputs=[chain_state, ex5, state], outputs=[chatbot, state, message])
 
+      predictBtn = gr.Button(value="Predict", visible=False)
+      predictBtn.click(predict, inputs=[message], outputs=[message])
+
 block.launch(debug=True)