Create Chat_with_pdf_OpenAI

Chat_with_pdf_OpenAI

@@ -0,0 +1,63 @@
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+from langchain.callbacks import get_openai_callback
+import os
+import openai
+from streamlit_chat import message
+
+OPENAI_API_KEY = st.text_input("Input your OpenAI API key", "")
+os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+# st.header("Ask your PDF 💬")
+
+# upload file
+pdf = st.file_uploader("Upload your PDF", type="pdf")
+
+# extract the text
+if pdf is not None:
+pdf_reader = PdfReader(pdf)
+text = ""
+for page in pdf_reader.pages:
+text += page.extract_text()
+
+# split into chunks
+text_splitter = CharacterTextSplitter(
+separator="\n",
+chunk_size=1000,
+chunk_overlap=200,
+length_function=len
+)
+chunks = text_splitter.split_text(text)
+
+# create embeddings
+embeddings = OpenAIEmbeddings()
+knowledge_base = FAISS.from_texts(chunks, embeddings)
+
+if 'generated' not in st.session_state:
+    st.session_state['generated'] = []
+if 'past' not in st.session_state:
+    st.session_state['past'] = []
+
+# show user input
+user_question = st.text_input("Ask a question about your PDF:")
+if user_question:
+docs = knowledge_base.similarity_search(user_question)
+
+llm = OpenAI()
+chain = load_qa_chain(llm, chain_type="stuff")
+with get_openai_callback() as cb:
+    response = chain.run(input_documents=docs, question=user_question)
+    print(cb)
+    
+#st.write(response)
+st.session_state.past.append(user_question)
+st.session_state.generated.append(response)
+    
+if st.session_state['generated']:
+for i in range(len(st.session_state['generated'])-1, -1, -1):
+    message(st.session_state["generated"][i], key=str(i))
+    message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')