Update app.py

app.py

@@ -4,13 +4,11 @@ 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.embeddings import HuggingFaceEmbeddings  # Using Hugging Face for embeddings
 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
+from langchain_groq import ChatGroq  # Using Groq LLaMA 3 model
 
 # Load environment variables
 load_dotenv()
@@ -18,7 +16,7 @@ load_dotenv()
 # Set up logging
 logging.basicConfig(
     level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s'
+    format="%(asctime)s - %(levelname)s - %(message)s"
 )
 
 # Function to extract text from PDF files
@@ -27,7 +25,7 @@ def get_pdf_text(pdf_docs):
     for pdf in pdf_docs:
         pdf_reader = PdfReader(pdf)
         for page in pdf_reader.pages:
-            text += page.extract_text()
+            text += page.extract_text() or ""  # Ensure no NoneType error
     return text
 
 # Function to split the extracted text into chunks
@@ -38,27 +36,19 @@ def get_text_chunks(text):
         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
+    return text_splitter.split_text(text)
 
+# Function to create a FAISS vectorstore using Hugging Face embeddings
 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)
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
     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-3.3-70b-versatile", temperature=0.5)
-        memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
+        memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
         
         conversation_chain = ConversationalRetrievalChain.from_llm(
             llm=llm,
@@ -75,36 +65,35 @@ def get_conversation_chain(vectorstore):
 # 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']
+        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}")
+                st.write(f"**User:** {message.content}")
             else:
-                st.write(f"*Bot:* {message.content}")
+                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:")
+    st.set_page_config(page_title="Chat with PDFs", page_icon="📚")
 
     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 PDFs 📚")
     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")
+        st.subheader("Upload Your Documents")
         pdf_docs = st.file_uploader(
-            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True
+            "Upload your PDFs and click 'Process'", accept_multiple_files=True
         )
         if st.button("Process"):
             with st.spinner("Processing..."):
@@ -113,5 +102,5 @@ def main():
                 vectorstore = get_vectorstore(text_chunks)
                 st.session_state.conversation = get_conversation_chain(vectorstore)
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()