Create app.py

app.py

@@ -0,0 +1,154 @@
+# importing necessary libraries
+import os
+import time
+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain_openai import OpenAI, OpenAIEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+ 
+
+# load the environment variables into the python script
+load_dotenv() 
+# fetching the openai_api_key environment variable
+openai_api_key = os.getenv('OPENAI_API_KEY')
+
+
+# Initialize session states
+if 'vectorDB' not in st.session_state:
+    st.session_state.vectorDB = None
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+if 'bot_name' not in st.session_state:
+    st.session_state.bot_name = ''
+if 'chain' not in st.session_state:
+    st.session_state.chain = None
+
+
+def get_pdf_text(pdf) -> str:
+    """ This function extracts the text from the PDF file """
+    text = ""
+    pdf_reader = PdfReader(pdf)
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+
+    return text
+
+def get_vectorstore(text_chunks):
+    """ This function will create a vector database as well as create and store the embedding of the text chunks into the VectorDB """
+    embeddings = OpenAIEmbeddings()
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+def get_text_chunks(text: str):
+    """ This function will split the text into the smaller chunks"""
+    text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size=1000,
+        chunk_overlap=264,
+        length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+def processing(pdf):
+    """This function divides the PDF into smaller chunks and saves these segmented chunks in a vector database. And return the Vector Database"""
+    # getting all the raw text from the PDF
+    raw_text = get_pdf_text(pdf)
+
+    # divinding the raw text into smaller chunks
+    text_chunks = get_text_chunks(raw_text)
+
+    # Creating and storing the chunks in vector database
+    vectorDB = get_vectorstore(text_chunks)
+
+    return vectorDB
+
+def get_response(query: str) -> str:
+    # getting the context from the database that is similar to the user query
+    query_context = st.session_state.vectorDB.similarity_search(query=query,k=2)
+    # calling the chain to get the output from the LLM
+    response = st.session_state.chain.invoke({'human_input':query,'context':query_context,'name':st.session_state.bot_name})['text']
+    # Iterate through each word in the 'response' string after splitting it based on whitespace
+    for word in response.split():
+        # Yield the current word followed by a space, effectively creating a generator
+        yield word + " "
+    
+        # Pause execution for 0.05 seconds (50 milliseconds) to introduce a delay
+        time.sleep(0.05)
+
+def get_conversation_chain(vectorDB):
+    # using OPENAI LLM
+    llm = OpenAI(temperature=0.5)
+
+    # creating a template to pass into LLM
+    template = """You are a Personalized ChatBot with a name: {name} for a company's customer support system, aiming to enhance the customer experience by providing tailored assistance and information. You are interacting with customer.
+
+    Answer the question as detailed as possible from the context: {context}\n , and  make sure to provide all the details, if the answer is not in the provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
+
+    {chat_history}
+    Human: {human_input}
+    AI: """
+
+    # creating a prompt that is used to format the input of the user
+    prompt = PromptTemplate(template = template,input_variables=['chat_history','human_input','name','context'])
+
+    # creating a memory that will store the chat history between chatbot and user
+    memory = ConversationBufferWindowMemory(memory_key='chat_history',input_key="human_input",k=5)
+
+    chain = LLMChain(llm=llm,prompt=prompt,memory=memory,verbose=True) 
+
+    return chain
+
+
+
+if __name__ =='__main__':
+    #setting the config of WebPage
+    st.set_page_config(page_title="Personalized ChatBot",page_icon="🤖")
+    st.header('Personalized Customer Support Chatbot 🤖',divider='rainbow')
+    
+    # taking input( bot name and pdf file) from the user
+    with st.sidebar:
+        st.caption('Please enter the **Bot Name** and Upload **PDF** File!')
+
+        bot_name = st.text_input(label='Bot Name',placeholder='Enter the bot name here....',key="bot_name")
+        file = st.file_uploader("Upload a PDF file!",type='pdf')
+
+        # moving forward only when both the inputs are given by the user
+        if file and bot_name:
+            # the Process File button will process the pdf file and save the chunks into the vector database
+            if st.button('Process File'):
+                # if there is existing chat history we will delete it 
+                if st.session_state.messages != []:
+                    st.session_state.messages = []
+                
+                with st.spinner('Processing.....'):
+                    st.session_state['vectorDB'] = processing(file)
+                    st.session_state['chain'] = get_conversation_chain(st.session_state['vectorDB'])
+                st.write('File Processed')
+                
+
+    # if the vector database is ready to use then only show the chatbot interface
+    if st.session_state.vectorDB: 
+        # Display chat messages from history on app rerun
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.write(message["content"])
+        
+        # taking the input i.e. query from the user (walrun operator)
+        if prompt := st.chat_input(f"Message {st.session_state.bot_name}"):
+            # Add user message to chat history
+            st.session_state.messages.append({"role": "user", "content": prompt})
+            # Display user message in chat message container
+            with st.chat_message("user"):
+                st.write(prompt)
+
+            # Display assistant response in chat message container
+            with st.chat_message("assistant"):
+                response = st.write_stream(get_response(prompt))
+            # Add assistant response to chat history
+            st.session_state.messages.append({"role": "assistant", "content": response})