Create app.py

app.py

@@ -0,0 +1,108 @@
+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_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+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() or ""
+    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
+    )
+    return text_splitter.split_text(text)
+
+# Function to create a FAISS vectorstore using Hugging Face embeddings
+def get_vectorstore(text_chunks):
+    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:
+        groq_api_key = os.getenv("GROQ_API_KEY")
+        llm = ChatGroq(model="llama3-8b-8192", api_key=groq_api_key, 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:
+        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="📚")
+
+    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 📚")
+    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()