Update app.py

app.py

@@ -1,169 +1,16 @@
-
-# import os
-# import logging
-# from dotenv import load_dotenv
-# import streamlit as st
-# from PyPDF2 import PdfReader
-# from langchain.text_splitter import CharacterTextSplitter
-# # from langchain.embeddings import HuggingFaceInstructEmbeddings
-# from langchain_cohere import CohereEmbeddings
-# from langchain.vectorstores import FAISS
-# from langchain.memory import ConversationBufferMemory
-# from langchain.chains import ConversationalRetrievalChain
-# # from langchain.llms import Ollama
-# from langchain_groq import ChatGroq
-
-# # Load environment variables
-# load_dotenv()
-
-# # Set up logging
-# logging.basicConfig(
-#     level=logging.INFO,
-#     format='%(asctime)s - %(levelname)s - %(message)s'
-# )
-
-# # Function to extract text from PDF files
-# 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
-
-# # Function to split the extracted text into chunks
-# 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
-
-# # Function to create a FAISS vectorstore
-# # 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_vectorstore(text_chunks):
-#     cohere_api_key = os.getenv("COHERE_API_KEY")
-#     embeddings = CohereEmbeddings(model="embed-english-v3.0", cohere_api_key=cohere_api_key)
-#     vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-#     return vectorstore
-
-# # Function to set up the conversational retrieval chain
-# def get_conversation_chain(vectorstore):
-#     try:
-#         # llm = Ollama(model="llama3.2:1b")
-#         llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0.5)
-#         memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
-        
-#         conversation_chain = ConversationalRetrievalChain.from_llm(
-#             llm=llm,
-#             retriever=vectorstore.as_retriever(),
-#             memory=memory
-#         )
-        
-#         logging.info("Conversation chain created successfully.")
-#         return conversation_chain
-#     except Exception as e:
-#         logging.error(f"Error creating conversation chain: {e}")
-#         st.error("An error occurred while setting up the conversation chain.")
-
-# # Handle user input
-# def handle_userinput(user_question):
-#     if st.session_state.conversation is not None:
-#         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(f"*User:* {message.content}")
-#             else:
-#                 st.write(f"*Bot:* {message.content}")
-#     else:
-#         st.warning("Please process the documents first.")
-
-# # Main function to run the Streamlit app
-# def main():
-#     load_dotenv()
-#     st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
-
-#     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 = None
-
-#     st.header("Chat with multiple PDFs :books:")
-#     user_question = st.text_input("Ask a question about your documents:")
-#     if user_question:
-#         handle_userinput(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 = get_pdf_text(pdf_docs)
-#                 text_chunks = get_text_chunks(raw_text)
-#                 vectorstore = get_vectorstore(text_chunks)
-#                 st.session_state.conversation = get_conversation_chain(vectorstore)
-
-# if __name__ == '__main__':
-#     main()
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 import os
 import logging
 from dotenv import load_dotenv
 import streamlit as st
 from PyPDF2 import PdfReader
-from docx import Document  # Import for handling Word files
-import io  # Import for handling byte streams
 from langchain.text_splitter import CharacterTextSplitter
+# from langchain.embeddings import HuggingFaceInstructEmbeddings
 from langchain_cohere import CohereEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
+# from langchain.llms import Ollama
 from langchain_groq import ChatGroq
 
 # Load environment variables
@@ -184,22 +31,6 @@ def get_pdf_text(pdf_docs):
             text += page.extract_text()
     return text
 
-# Function to extract text from Word files
-def get_word_text(word_docs):
-    text = ""
-    for word in word_docs:
-        doc = Document(io.BytesIO(word.read()))  # Read the Word document from bytes
-        for para in doc.paragraphs:
-            text += para.text + "\n"  # Append each paragraph followed by a newline
-    return text
-
-# Function to extract text from TXT files
-def get_txt_text(txt_docs):
-    text = ""
-    for txt in txt_docs:
-        text += txt.read().decode("utf-8") + "\n"  # Read and decode the text file content
-    return text
-
 # Function to split the extracted text into chunks
 def get_text_chunks(text):
     text_splitter = CharacterTextSplitter(
@@ -211,6 +42,12 @@ def get_text_chunks(text):
     chunks = text_splitter.split_text(text)
     return chunks
 
+# Function to create a FAISS vectorstore
+# 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_vectorstore(text_chunks):
     cohere_api_key = os.getenv("COHERE_API_KEY")
     embeddings = CohereEmbeddings(model="embed-english-v3.0", cohere_api_key=cohere_api_key)
@@ -220,6 +57,7 @@ def get_vectorstore(text_chunks):
 # Function to set up the conversational retrieval chain
 def get_conversation_chain(vectorstore):
     try:
+        # llm = Ollama(model="llama3.2:1b")
         llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0.5)
         memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
         
@@ -252,54 +90,56 @@ def handle_userinput(user_question):
 # Main function to run the Streamlit app
 def main():
     load_dotenv()
-    st.set_page_config(page_title="Chat with multiple documents", page_icon=":books:")
+    st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
 
     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 = None
 
-    st.header("Chat with multiple documents :books:")
-    
+    st.header("Chat with multiple PDFs :books:")
     user_question = st.text_input("Ask a question about your documents:")
-    
     if user_question:
         handle_userinput(user_question)
 
     with st.sidebar:
         st.subheader("Your documents")
-        
         pdf_docs = st.file_uploader(
-            "Upload your PDFs here", accept_multiple_files=True, type=["pdf"]
-        )
-        
-        word_docs = st.file_uploader(
-            "Upload your Word documents here", accept_multiple_files=True, type=["docx"]
-        )
-        
-        txt_docs = st.file_uploader(
-            "Upload your TXT files here", accept_multiple_files=True, type=["txt"]
+            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True
         )
-        
         if st.button("Process"):
             with st.spinner("Processing..."):
-                raw_text = ""
-                
-                if pdf_docs:
-                    raw_text += get_pdf_text(pdf_docs)
-                
-                if word_docs:
-                    raw_text += get_word_text(word_docs)
-                
-                if txt_docs:
-                    raw_text += get_txt_text(txt_docs)
-                
-                if raw_text:  # Only process if there is any raw text extracted.
-                    text_chunks = get_text_chunks(raw_text)
-                    vectorstore = get_vectorstore(text_chunks)
-                    st.session_state.conversation = get_conversation_chain(vectorstore)
-                else:
-                    st.warning("No documents were uploaded or processed.")
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                vectorstore = get_vectorstore(text_chunks)
+                st.session_state.conversation = get_conversation_chain(vectorstore)
 
 if __name__ == '__main__':
     main()
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