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import streamlit as st
from streamlit_js_eval import streamlit_js_eval
import choosingdata as choice
from dotenv import load_dotenv
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_openai import ChatOpenAI
from langchain_openai import OpenAIEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain_community.llms import HuggingFaceHub
def get_text_chunks(text):
"""
Splits the given text into chunks based on specified character settings.
Parameters:
- text (str): The text to be split into chunks.
Returns:
- list: A list of text chunks.
"""
text_splitter = CharacterTextSplitter(
separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len
)
chunks = text_splitter.split_text(text)
return chunks
def get_vectorstore(text_chunks):
"""
Generates a vector store from a list of text chunks using specified embeddings.
Parameters:
- text_chunks (list of str): Text segments to convert into vector embeddings.
Returns:
- FAISS: A FAISS vector store containing the embeddings of the text chunks.
"""
embeddings = OpenAIEmbeddings(
openai_api_base="https://openai.vocareum.com/v1",
)
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
"""
Initializes a conversational retrieval chain that uses a large language model
for generating responses based on the provided vector store.
Parameters:
- vectorstore (FAISS): A vector store to be used for retrieving relevant content.
Returns:
- ConversationalRetrievalChain: An initialized conversational chain object.
"""
llm = ChatOpenAI(
model_name="gpt-4-1106-preview",
openai_api_base="https://openai.vocareum.com/v1",
)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm, retriever=vectorstore.as_retriever(), memory=memory
)
return conversation_chain
def set_prompt(text_block):
"""
Callback function that sets the chosen prompt in the session state.
Parameters:
- text_block (str): The prompt text selected by the user.
"""
st.session_state["messages"].append({"role": "user", "content": text_block})
st.session_state["prompts"] = text_block
def prompts():
"""
Renders clickable buttons for predefined prompts in the Streamlit application,
allowing the user to select a prompt to send to the conversation chain.
"""
potential_prompts = [
f"What is the meaning of the song {st.session_state['title']} by {st.session_state['artist']}?",
f"What is the most difficult English grammar point in the song {st.session_state['title']} by {st.session_state['artist']}? Can you explain it?",
f"What is the most common English word in the song {st.session_state['title']} by {st.session_state['artist']} (excluding stopwords)? Can you give some example sentences using that word?",
f"What is the most worth learning English phrase in the song {st.session_state['title']} by {st.session_state['artist']}? Can you explain it and provide practical example using the phrase?",
]
chosen_prompt = None
for index, text_block in enumerate(potential_prompts):
st.button(
f"Prompt {index + 1}: {text_block}", on_click=set_prompt, args=(text_block,)
)
def get_lyrics():
"""
Retrieves the lyrics stored in the session state.
Returns:
- str: The lyrics of the currently selected song.
"""
lyrics = st.session_state["lyrics"]
return lyrics
def page_title():
"""
Sets the title of the Streamlit page based on the selected song and artist.
"""
if st.session_state["title"] and st.session_state["artist"]:
st.title(
f'🎵 English Music Recommender 💬 ({st.session_state["title"]} by {st.session_state["artist"]})'
)
else:
st.title("🎵 English Music Recommender 💬")
def chat_sidebar():
"""
Renders the sidebar in the Streamlit application for selecting music preferences
and handling song recommendations.
"""
with st.sidebar:
st.title("💚 Music Preferences")
user_difficulty = st.sidebar.radio(
"Choose a difficulty level:", ("Easy", "Medium", "Hard")
)
user_danceability = st.sidebar.radio(
"How much do you want to dance?", ("Low", "Medium", "High")
)
user_valence = st.sidebar.radio(
"What energy are you feeling?", ("Negative", "Neutral", "Positive")
)
if not st.session_state["song_bool"]:
if st.sidebar.button("Submit"):
recommendations = choice.recommendation(
choice.df,
dance_choice=user_danceability,
valence_choice=user_valence,
difficulty_choice=user_difficulty,
)
st.session_state["title"] = recommendations["title"].values[0]
st.session_state["artist"] = recommendations["artist"].values[0]
st.session_state["lyrics"] = recommendations["lyrics"].values[0]
st.session_state["id"] = (
f'https://open.spotify.com/track/{recommendations["id"].values[0]}'
)
st.session_state["song_bool"] = True
st.rerun()
else:
if st.session_state["song_bool"]:
st.write("### We would recommend you...")
st.write(f"## {st.session_state['title']}")
st.write(f" by {st.session_state['artist']}")
st.markdown(
f'<a href="{st.session_state["id"]}"><img src="{st.session_state["icon"]}" alt="Clickable image" style="height:60px;"></a>',
unsafe_allow_html=True,
)
st.write("Please refresh the page for a new recommendation.")
if st.button("Reload page"):
streamlit_js_eval(js_expressions="parent.window.location.reload()")
def chat():
"""
Manages the chat interface in the Streamlit application, handling the conversation
flow and displaying the chat history.
"""
if st.session_state["lyrics"]:
text_chunks = get_text_chunks(get_lyrics())
vectorstore = get_vectorstore(text_chunks)
st.session_state.conversation = get_conversation_chain(vectorstore)
if len(st.session_state.messages) == 1:
message = st.session_state.messages[0]
with st.chat_message(message["role"]):
st.write(message["content"])
prompts()
else:
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
# User-provided prompt
if prompt := st.chat_input():
st.session_state.messages.append({"role": "user", "content": prompt})
st.session_state.prompts = prompt
with st.chat_message("user"):
st.write(prompt)
if st.session_state.messages[-1]["role"] != "system":
with st.chat_message("system"):
with st.spinner("Generating response..."):
response = st.session_state.conversation.invoke(
{"question": st.session_state.prompts}
)
st.session_state.chat_history = response["chat_history"]
message = st.session_state.chat_history[-1]
st.write(message.content)
message = {"role": "system", "content": message.content}
st.session_state.messages.append(message)
else:
st.write("You can chat with GPT once a song has been recommended to you!")
def init():
"""
Initializes the session state variables used in the Streamlit application and
loads environment variables.
"""
load_dotenv()
if "title" not in st.session_state:
st.session_state["title"] = ""
if "artist" not in st.session_state:
st.session_state["artist"] = ""
if "icon" not in st.session_state:
st.session_state["icon"] = (
"https://thereceptionist.com/wp-content/uploads/2021/02/Podcast-Listen-On-Spotify-1.png"
)
if "id" not in st.session_state:
st.session_state["id"] = ""
if "song_bool" not in st.session_state:
st.session_state["song_bool"] = False
if "messages" not in st.session_state.keys():
st.session_state.messages = [
{
"role": "system",
"content": "What do you want to learn about? Here are some suggested prompts: ",
}
]
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
if "lyrics" not in st.session_state:
st.session_state["lyrics"] = ""
if "prompts" not in st.session_state:
st.session_state["prompts"] = ""
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