Create app.py

app.py

@@ -0,0 +1,165 @@
+from dataclasses import dataclass
+from typing import Literal
+import streamlit as st
+from langchain import OpenAI
+from langchain.callbacks import get_openai_callback
+from langchain.chains import ConversationChain
+from langchain.chains.conversation.memory import ConversationSummaryMemory
+import streamlit.components.v1 as components
+
+@dataclass
+class Message:
+    """Class for keeping track of a chat message."""
+    origin: Literal["human", "ai"]
+    message: str
+
+def load_css():
+    with open("static/styles.css", "r") as f:
+        css = f"<style>{f.read()}</style>"
+        st.markdown(css, unsafe_allow_html=True)
+
+def initialize_session_state():
+    if "history" not in st.session_state:
+        st.session_state.history = []
+    if "token_count" not in st.session_state:
+        st.session_state.token_count = 0
+    if 'chain' not in st.session_state:
+        llm = ChatOpenAI(model_name="gpt-3.5-turbo-0613", temperature=0)
+        loader = PyPDFLoader("/home/user/app/docs.pdf")
+        documents = loader.load()
+
+        text_splitter = CharacterTextSplitter(chunk_size=800, chunk_overlap=0)
+        texts = text_splitter.split_documents(documents)
+
+        embeddings = OpenAIEmbeddings()
+        vector_store = Chroma.from_documents(texts, embeddings)
+        retriever = vector_store.as_retriever(search_kwargs={"k": 3})
+
+        from langchain.prompts.chat import (
+            ChatPromptTemplate,
+            SystemMessagePromptTemplate,
+            HumanMessagePromptTemplate,
+        )
+
+        system_template="""You act like a successful pharmacist. Talk to students about the career path of a pharmacist. 
+        Never reveal that it is an AI. Use the following pieces of context to answer the users question shortly.
+        Given the following summaries of a long document and a question, create a final answer with references.
+        If you don't know the answer, just say that "I don't know", don't try to make up an answer.
+        ----------------
+        {summaries}
+        You MUST answer in Korean and in Markdown format"""
+
+        messages = [
+            SystemMessagePromptTemplate.from_template(system_template),
+            HumanMessagePromptTemplate.from_template("{question}")
+        ]
+
+        prompt = ChatPromptTemplate.from_messages(messages)
+
+        chain_type_kwargs = {"prompt": prompt}
+
+        st.session_state['chain'] = RetrievalQAWithSourcesChain.from_chain_type(
+            llm=llm,
+            chain_type="stuff",
+            retriever=retriever,
+            return_source_documents=True,
+            chain_type_kwargs=chain_type_kwargs,
+            reduce_k_below_max_tokens=True,
+            verbose=True,
+        )
+
+def generate_response(user_input):  
+    result = st.session_state['chain'](user_input)
+
+    bot_message = result['answer']
+
+    for i, doc in enumerate(result['source_documents']):
+        bot_message += '[' + str(i+1) + '] ' + doc.metadata['source'] + '(' + str(doc.metadata['page']) + ') '
+
+    return bot_message
+
+def on_click_callback():
+    with get_openai_callback() as cb:
+        human_prompt = st.session_state.human_prompt
+        llm_response = generate_response(human_prompt)
+        st.session_state.history.append(
+            Message("human", human_prompt)
+        )
+        st.session_state.history.append(
+            Message("ai", llm_response)
+        )
+        st.session_state.token_count += cb.total_tokens
+
+load_css()
+initialize_session_state()
+
+st.title("Hello Custom CSS Chatbot 🤖")
+
+chat_placeholder = st.container()
+prompt_placeholder = st.form("chat-form")
+credit_card_placeholder = st.empty()
+
+with chat_placeholder:
+    for chat in st.session_state.history:
+        div = f"""
+<div class="chat-row 
+    {'' if chat.origin == 'ai' else 'row-reverse'}">
+    <img class="chat-icon" src="app/static/{
+        'ai_icon.png' if chat.origin == 'ai' 
+                      else 'user_icon.png'}"
+         width=32 height=32>
+    <div class="chat-bubble
+    {'ai-bubble' if chat.origin == 'ai' else 'human-bubble'}">
+        &#8203;{chat.message}
+    </div>
+</div>
+        """
+        st.markdown(div, unsafe_allow_html=True)
+    
+    for _ in range(3):
+        st.markdown("")
+
+with prompt_placeholder:
+    st.markdown("**Chat**")
+    cols = st.columns((6, 1))
+    cols[0].text_input(
+        "Chat",
+        value="Hello bot",
+        label_visibility="collapsed",
+        key="human_prompt",
+    )
+    cols[1].form_submit_button(
+        "Submit", 
+        type="primary", 
+        on_click=on_click_callback, 
+    )
+
+credit_card_placeholder.caption(f"""
+Used {st.session_state.token_count} tokens \n
+Debug Langchain conversation: 
+{st.session_state.conversation.memory.buffer}
+""")
+
+components.html("""
+<script>
+const streamlitDoc = window.parent.document;
+
+const buttons = Array.from(
+    streamlitDoc.querySelectorAll('.stButton > button')
+);
+const submitButton = buttons.find(
+    el => el.innerText === 'Submit'
+);
+
+streamlitDoc.addEventListener('keydown', function(e) {
+    switch (e.key) {
+        case 'Enter':
+            submitButton.click();
+            break;
+    }
+});
+</script>
+""", 
+    height=0,
+    width=0,
+)