Create app.py

app.py

@@ -0,0 +1,53 @@
+from langchain.chains import RetrievalQA
+from langchain.chains import RetrievalQAWithSourcesChain
+from langchain.document_loaders import TextLoader
+from langchain.docstore.document import Document
+import openai
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.llms import OpenAI
+import cohere
+from langchain.embeddings.cohere import CohereEmbeddings
+from langchain.llms import Cohere
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Chroma
+import os
+from tqdm import tqdm
+
+documents=[]
+path='./bios/'
+# path='./augBios/'
+for file in os.listdir(path):
+    loader = TextLoader(f'{path}{file}',encoding='unicode_escape')
+    # loader.load()[0].metadata['category']='biography'
+    # print(loader.load()[0].metadata)
+    documents += loader.load()
+# print(documents)
+print(len(documents))
+text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+texts = text_splitter.split_documents(documents)
+
+# embeddings = OpenAIEmbeddings()
+embeddings = CohereEmbeddings()
+docsearch = Chroma.from_documents(texts, embeddings)
+
+qa = RetrievalQA.from_chain_type(llm=Cohere(), chain_type="stuff", \
+    retriever=docsearch.as_retriever(search_kwargs={'k':1}),return_source_documents=True)
+
+def predict(message, history):
+    # history_langchain_format = []
+    # for human, ai in history:
+    #     history_langchain_format.append(HumanMessage(content=human))
+    #     history_langchain_format.append(AIMessage(content=ai))
+    # history_langchain_format.append(HumanMessage(content=message))
+    # gpt_response = llm(history_langchain_format)
+    # return gpt_response.content
+    message+='? just give me the book title-Author'
+    result = qa({"query": message})
+    # r1=docsearch.similarity_search_with_score(query=q,k=3)
+    # print([(item[-2].metadata,item[-1]) for item in r1],\
+    #       '\n\n',result['result'],f'|| {result["source_documents"][0].metadata}','\n*****\n')
+    return result['result'],f'|| source is==> {result["source_documents"][0].metadata}'
+    
+    
+
+gr.ChatInterface(predict).launch()