Update app.py

app.py

@@ -11,6 +11,7 @@ from langchain.llms import Cohere
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.vectorstores import Chroma
 import os
+import pickle
 from tqdm import tqdm
 import gradio as gr
 # from langchain.memory import ConversationSummaryMemory
@@ -44,6 +45,8 @@ docsearch = Chroma.from_documents(texts, embeddings)
 qa = RetrievalQA.from_chain_type(llm=Cohere(model='command'), chain_type="stuff", \
     retriever=docsearch.as_retriever(search_kwargs={'k':1}),return_source_documents=True)
 
+btuTuples=pickle.load(open('./bookTitleUrlTuples.pkl','rb'))
+bookTitleUrlDict={x:y for x,y in btuTuples}
 def predict(message, history):
     '''experimenation with memory and conversation retrieval chain has resulted in less
     performance, usefulness, and more halucination. Hence, this chat bot provides one 
@@ -69,9 +72,11 @@ def predict(message, history):
     # print([(item[-2].metadata,item[-1]) for item in r1],\
     #       '\n\n',result['result'],f'|| {result["source_documents"][0].metadata}','\n*****\n')
     if result['result'] not in ["I don't know","I don't know."]:
-        return result['result']+f'\n---\nIgnore the description below if the chatbot was unsure about its response \
-        or if the response is not about the book shown below\nAmazon Kindle ebook description is:\n {result["source_documents"][0].page_content}'+\
-        f'\nfrom this file: {result["source_documents"][0].metadata}'
+        bookNamePath=result["source_documents"][0].metadata["source"]
+        return result['result']+f'\n---\n*Ignore the description below if the chatbot was unsure about its response \
+        or if the response is not about the book shown below*\nAmazon Kindle ebook description is:\n {result["source_documents"][0].page_content}'+\
+        f'\n**from this file:** {bookNamePath}\n'+\
+        f'**link==> {bookTitleUrlDict[bookNamePath.split("/")[-1]]}'
     else:
         return result['result']