Update Chat_with_pdf_OpenAI

Chat_with_pdf_OpenAI

@@ -10,54 +10,55 @@ 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)
+def openai_pdf():
+    OPENAI_API_KEY = st.text_input("Input your OpenAI API key", "")
+    os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+    # st.header("Ask your PDF 💬")
     
-#st.write(response)
-st.session_state.past.append(user_question)
-st.session_state.generated.append(response)
+    # upload file
+    pdf = st.file_uploader("Upload your PDF", type="pdf")
     
-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')
+    # 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')