Update app.py

app.py

@@ -185,7 +185,56 @@ def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):
         conversation.append({'role': 'assistant', 'content': file_content})
     response = openai.ChatCompletion.create(model=model_choice, messages=conversation)
     return response['choices'][0]['message']['content']
-        
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+
+def extract_text_from_pdfs(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+def split_text_into_chunks(text):
+    text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
+    return text_splitter.split_text(text)
+
+def create_vector_store_from_text_chunks(text_chunks):
+    key = os.getenv('OPENAI_KEY')
+    embeddings = OpenAIEmbeddings(openai_api_key=key)
+    return FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+
+def create_conversation_chain(vectorstore):
+    llm = ChatOpenAI()
+    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
+    return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)
+
+def process_user_input(user_question):
+    response = st.session_state.conversation({'question': user_question})
+    st.session_state.chat_history = response['chat_history']
+
+    for i, message in enumerate(st.session_state.chat_history):
+        template = user_template if i % 2 == 0 else bot_template
+        st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+
+
+
+
+
+
+
     
 def main():
     # Sidebar and global
@@ -306,63 +355,20 @@ if __name__ == "__main__":
 
 
 
+load_dotenv()
+st.write(css, unsafe_allow_html=True)
 
+st.header("Chat with multiple PDFs :books:")
+user_question = st.text_input("Ask a question about your documents:")
+if user_question:
+    process_user_input(user_question)
 
-
-
-
-
-
-
-def extract_text_from_pdfs(pdf_docs):
-    text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
-
-def split_text_into_chunks(text):
-    text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
-    return text_splitter.split_text(text)
-
-def create_vector_store_from_text_chunks(text_chunks):
-    key = os.getenv('OPENAI_KEY')
-    embeddings = OpenAIEmbeddings(openai_api_key=key)
-    return FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-
-def create_conversation_chain(vectorstore):
-    llm = ChatOpenAI()
-    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
-    return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)
-
-def process_user_input(user_question):
-    response = st.session_state.conversation({'question': user_question})
-    st.session_state.chat_history = response['chat_history']
-
-    for i, message in enumerate(st.session_state.chat_history):
-        template = user_template if i % 2 == 0 else bot_template
-        st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
-
-#def main():
-    load_dotenv()
-    #st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
-    st.write(css, unsafe_allow_html=True)
-
-    st.header("Chat with multiple PDFs :books:")
-    user_question = st.text_input("Ask a question about your documents:")
-    if user_question:
-        process_user_input(user_question)
-
-    with st.sidebar:
-        st.subheader("Your documents")
-        pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
-        if st.button("Process"):
-            with st.spinner("Processing"):
-                raw_text = extract_text_from_pdfs(pdf_docs)
-                text_chunks = split_text_into_chunks(raw_text)
-                vectorstore = create_vector_store_from_text_chunks(text_chunks)
-                st.session_state.conversation = create_conversation_chain(vectorstore)
-
-#if __name__ == '__main__':
-#    main()
+with st.sidebar:
+    st.subheader("Your documents")
+    pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
+    if st.button("Process"):
+        with st.spinner("Processing"):
+            raw_text = extract_text_from_pdfs(pdf_docs)
+            text_chunks = split_text_into_chunks(raw_text)
+            vectorstore = create_vector_store_from_text_chunks(text_chunks)
+            st.session_state.conversation = create_conversation_chain(vectorstore)