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src/app.py

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+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain_groq import ChatGroq
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+#from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.llms import HuggingFaceHub
+from langchain.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from htmlTemplates import css, bot_template, user_template 
+
+
+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_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_vector_store(text_chunks):
+    model_name = "BAAI/bge-small-en"
+    model_kwargs = {'device': 'cpu'}
+    encode_kwargs = {"normalize_embeddings": True}
+    
+    embeddings = HuggingFaceEmbeddings(
+        model_name=model_name,
+        model_kwargs=model_kwargs,
+        encode_kwargs=encode_kwargs
+    )
+    
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+def get_conversation_chain(vectorstore):
+    llm= ChatGroq(model="llama3-8b-8192",temperature=0) 
+    # Create the prompt template
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", """You are a helpful assistant answering questions based on the provided documents.
+        Answer the question using only the context provided.
+        If you don't know the answer, just say that you don't know, don't try to make up an answer.
+        Keep your answers focused and relevant to the question."""),
+        ("human", """Context: {context}
+
+Question: {question}
+
+Answer: """)
+    ])
+
+    # Create the retrieval chain using syntax
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
+    
+    # Define the chain
+    chain = (
+        {"context": retriever, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+
+    return chain
+
+def handle_user_input(user_question):
+    if st.session_state.conversation is None:
+        st.warning("Please upload and process documents first.")
+        return
+
+    try:
+        # Invoke the chain with the question
+        response = st.session_state.conversation.invoke(user_question)
+        
+        # Update chat history
+        if 'chat_history' not in st.session_state:
+            st.session_state.chat_history = []
+        
+        # Add the new messages to chat history
+        st.session_state.chat_history.append(("user", user_question))
+        st.session_state.chat_history.append(("bot", response))
+
+        # Display chat history
+        for sender, message in st.session_state.chat_history:
+            if sender == "user":
+                st.write(user_template.replace("{{MSG}}", message), unsafe_allow_html=True)
+            else:
+                st.write(bot_template.replace("{{MSG}}", message), unsafe_allow_html=True)
+    
+    except Exception as e:
+        st.error(f"An error occurred while processing your question: {str(e)}")
+
+def main():
+    load_dotenv()
+    
+    # st.write(css, unsafe_allow_html=True)
+
+    if 'user_template' not in globals():
+        global user_template
+        user_template = '''
+        <div class="chat-message user">
+            <div class="avatar">
+                <img src="https://i.ibb.co/rdZC7LZ/user.png">
+            </div>
+            <div class="message">{{MSG}}</div>
+        </div>
+        '''
+
+    if 'bot_template' not in globals():
+        global bot_template
+        bot_template = '''
+        <div class="chat-message bot">
+            <div class="avatar">
+                <img src="https://i.ibb.co/cN0nmSj/robot.png">
+            </div>
+            <div class="message">{{MSG}}</div>
+        </div>
+        '''
+
+    st.set_page_config(page_title='Chat with 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('PDF ChatBot 📚')
+    
+    # Sidebar for PDF upload
+    with st.sidebar:
+        st.subheader("Upload Documents")
+        pdf_docs = st.file_uploader(
+            "Upload your PDFs here and click 'Process'",
+            accept_multiple_files=True,
+            type=['pdf']
+        )
+        
+        if st.button('Process'):
+            if not pdf_docs:
+                st.warning("Please upload at least one PDF document.")
+                return
+                
+            with st.spinner("Processing documents..."):
+                try:
+                    # 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_vector_store(text_chunks)
+                    
+                    # Create conversation chain
+                    st.session_state.conversation = get_conversation_chain(vectorstore)
+                    
+                    st.success("Documents processed successfully!")
+                except Exception as e:
+                    st.error(f"An error occurred: {str(e)}")
+
+    # Main chat interface
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_user_input(user_question)
+
+if __name__ == "__main__":
+    main()

src/htmlTemplates.py

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+css = '''
+<style>
+.chat-message {
+    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
+}
+.chat-message.user {
+    background-color: #2b313e
+}
+.chat-message.bot {
+    background-color: #475063
+}
+.chat-message .avatar {
+  width: 20%;
+}
+.chat-message .avatar img {
+  max-width: 78px;
+  max-height: 78px;
+  border-radius: 50%;
+  object-fit: cover;
+}
+.chat-message .message {
+  width: 80%;
+  padding: 0 1.5rem;
+  color: #fff;
+}
+'''
+
+bot_template = '''
+<div class="chat-message bot">
+    <div class="avatar">
+        <img src="https://logos-world.net/wp-content/uploads/2020/02/Iron-Man-Logo-500x281.png">
+    </div>
+    <div class="message">{{MSG}}</div>
+</div>
+'''
+
+user_template = '''
+<div class="chat-message user">
+    <div class="avatar">
+        <img src="https://cdn.freebiesupply.com/logos/large/2x/spider-man-4-logo-png-transparent.png">
+    </div>    
+    <div class="message">{{MSG}}</div>
+</div>
+'''