Create app.py

app.py

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+import streamlit as st
+from langchain_openai import ChatOpenAI
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_chroma import Chroma
+from langchain_openai import OpenAIEmbeddings
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.messages import HumanMessage
+
+# Set page config
+st.set_page_config(page_title="Tbank Assistant", layout="wide")
+
+# Streamlit app header
+st.title("Tbank Customer Support Chatbot")
+
+# Sidebar for API Key input
+with st.sidebar:
+    st.header("Configuration")
+    api_key = st.text_input("Enter your OpenAI API Key:", type="password")
+
+# Main app logic
+@st.cache_resource
+def initialize_components():
+        chat = ChatOpenAI(model="gpt-3.5-turbo-1106", temperature=0.2,api_key=api_key)
+
+        loader = WebBaseLoader("https://www.tbankltd.com/about-us")
+        data = loader.load()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        all_splits = text_splitter.split_documents(data)
+        vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())
+        retriever = vectorstore.as_retriever(k=4)
+
+        SYSTEM_TEMPLATE = """
+        You are a helpful assistant chatbot for Tbank. Your knowledge comes exclusively from the content of our website. Please follow these guidelines:
+
+        1. When user Greets start by greeting the user warmly. For example: "Hello! Welcome to Tbank. How can I assist you today?"
+        2. When answering questions, use only the information provided in the website content. Do not make up or infer information that isn't explicitly stated.
+        3. If a user asks a question that can be answered using the website content, provide a clear and concise response. Include relevant details, but try to keep answers brief and to the point.
+        4. If a user asks a question that cannot be answered using the website content, or if the question is unrelated to Tbank, respond politely with something like:
+           "I apologize, but I don't have information about that topic. My knowledge is limited to Tbank's products/services and the content on our website. Is there anything specific about [Company/Website Name] I can help you with?"
+        5. Always maintain a friendly and professional tone.
+        6. If you're unsure about an answer, it's okay to say so. You can respond with:
+           "I'm not entirely sure about that. To get the most accurate information, I'd recommend checking our website or contacting our customer support team."
+        7. If a user asks for personal opinions or subjective information, remind them that you're an AI assistant and can only provide factual information from the website.
+        8. End each interaction by asking if there's anything else you can help with related to Tbank.
+
+        Remember, your primary goal is to assist users with information directly related to Tbank and its website content. Stick to this information and avoid speculating or providing information from other sources.
+        If the user query is not in context, simply tell `We are sorry, we don't have information on this
+
+        <context>
+        {context}
+        </context>
+        """
+
+        question_answering_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    SYSTEM_TEMPLATE,
+                ),
+                MessagesPlaceholder(variable_name="messages"),
+            ]
+        )
+
+        document_chain = create_stuff_documents_chain(chat, question_answering_prompt)
+
+        return retriever, document_chain
+
+
+    # Load components
+with st.spinner("Initializing Tbank Assistant..."):
+    retriever, document_chain = initialize_components()
+
+# Chat interface
+st.subheader("Chat with Tbank Assistant")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# React to user input
+# React to user input
+if prompt := st.chat_input("What would you like to know about Tbank?"):
+    # Display user message in chat message container
+    st.chat_message("user").markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+
+        # Retrieve relevant documents
+        docs = retriever.get_relevant_documents(prompt)
+
+        # Generate response
+        response = document_chain.invoke(
+            {
+                "context": docs,
+                "messages": [
+                    HumanMessage(content=prompt)
+                ],
+            }
+        )
+
+        # The response is already a string, so we can use it directly
+        full_response = response
+        message_placeholder.markdown(full_response)
+
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": full_response})
+
+
+
+# Add a footer
+st.markdown("---")
+st.markdown("By AI Planet")