Update app.py

app.py

@@ -1,16 +1,169 @@
+
+# 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
@@ -31,6 +184,22 @@ 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(
@@ -42,12 +211,6 @@ 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)
@@ -57,7 +220,6 @@ 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)
         
@@ -90,29 +252,54 @@ 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 PDFs", page_icon=":books:")
+    st.set_page_config(page_title="Chat with multiple documents", 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:")
+    st.header("Chat with multiple documents :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
+            "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"]
         )
+        
         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)
+                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.")
 
 if __name__ == '__main__':
-    main()
+    main()