Update app.py

app.py

@@ -1,90 +1,90 @@
-import streamlit as st
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from langchain_text_splitters import CharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceInstructEmbeddings
-from langchain_community.vectorstores import FAISS 
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from langchain.llms import HuggingFaceHub
-from htmltemplates import css, bot_template, user_template
-
-def get_text_chunks(text):
-    text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size=1000, chunk_overlap=200,
-        length_function=len
-    )
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-def get_pdf_text(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 get_vectorstore(text_chunks):
-    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
-    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-    return vectorstore
-
-def get_conversation_chain(vectorstore):
-    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.5, "max_length": 512})
-    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-    conversation_chain = ConversationalRetrievalChain.from_llm(
-        llm=llm,
-        retriever=vectorstore.as_retriever(),
-        memory=memory
-    )
-    return conversation_chain
-
-def handle_userinput(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):
-        if i % 2 == 0:
-            st.write(user_template.replace("{{MSG}}", message["content"]), unsafe_allow_html=True)
-        else:
-            st.write(bot_template.replace("{{MSG}}", message["content"]), unsafe_allow_html=True)
-
-def main():
-    load_dotenv()
-    st.set_page_config(page_title="Chat with your PDFs", page_icon=":books:")
-    
-    st.write(css, unsafe_allow_html=True)
-    
-    if "conversation" not in st.session_state:
-        st.session_state.conversation = None
-    if "chat_history" not in st.session_state:
-        st.session_state.chat_history = []
-        
-    st.header("Chat with your PDFs :books:")
-    user_question = st.text_input("Ask a question about your documents:")
-    if user_question:
-        handle_userinput(user_question)
-        
-    st.write(user_template.replace("{{MSG}}", "Hello Robot"), unsafe_allow_html=True)
-    st.write(bot_template.replace("{{MSG}}", "Hello"), unsafe_allow_html=True)
-    
-    with st.sidebar:
-        st.subheader("Your Documents")
-        pdf_docs = st.file_uploader("Upload your PDFs here", accept_multiple_files=True)
-        if st.button("Enter"):
-            with st.spinner("Processing"):
-                # Get PDF text
-                raw_text = get_pdf_text(pdf_docs)
-                # Get text chunks
-                text_chunks = get_text_chunks(raw_text)
-                # Create vector store
-                vectorstore = get_vectorstore(text_chunks)
-                
-                # Initialize conversation chain
-                st.session_state.conversation = get_conversation_chain(vectorstore)
-                st.success("Conversation chain initialized successfully.")
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain_text_splitter import CharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+from langchain_community.vectorstores import FAISS 
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain.llms import HuggingFaceHub
+from htmltemplates import css, bot_template, user_template
+
+def get_text_chunks(text):
+    text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size=1000, chunk_overlap=200,
+        length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+def get_pdf_text(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 get_vectorstore(text_chunks):
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+def get_conversation_chain(vectorstore):
+    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.5, "max_length": 512})
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=vectorstore.as_retriever(),
+        memory=memory
+    )
+    return conversation_chain
+
+def handle_userinput(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):
+        if i % 2 == 0:
+            st.write(user_template.replace("{{MSG}}", message["content"]), unsafe_allow_html=True)
+        else:
+            st.write(bot_template.replace("{{MSG}}", message["content"]), unsafe_allow_html=True)
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Chat with your PDFs", page_icon=":books:")
+    
+    st.write(css, unsafe_allow_html=True)
+    
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = []
+        
+    st.header("Chat with your PDFs :books:")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(user_question)
+        
+    st.write(user_template.replace("{{MSG}}", "Hello Robot"), unsafe_allow_html=True)
+    st.write(bot_template.replace("{{MSG}}", "Hello"), unsafe_allow_html=True)
+    
+    with st.sidebar:
+        st.subheader("Your Documents")
+        pdf_docs = st.file_uploader("Upload your PDFs here", accept_multiple_files=True)
+        if st.button("Enter"):
+            with st.spinner("Processing"):
+                # Get PDF text
+                raw_text = get_pdf_text(pdf_docs)
+                # Get text chunks
+                text_chunks = get_text_chunks(raw_text)
+                # Create vector store
+                vectorstore = get_vectorstore(text_chunks)
+                
+                # Initialize conversation chain
+                st.session_state.conversation = get_conversation_chain(vectorstore)
+                st.success("Conversation chain initialized successfully.")
+
+if __name__ == "__main__":
+    main()