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from pathlib import Path
import streamlit as st
from dotenv import load_dotenv
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
load_dotenv()
import os
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from data import load_db
from names import DATASET_ID, MODEL_ID
import json
@st.cache_resource
def init():
# embeddings = OpenAIEmbeddings(model=MODEL_ID)
# dataset_path = f"hub://{os.environ['ACTIVELOOP_ORG_ID']}/{DATASET_ID}"
# db = load_db(
# dataset_path,
# embedding_function=embeddings,
# token=os.environ["ACTIVELOOP_TOKEN"],
# org_id=os.environ["ACTIVELOOP_ORG_ID"],
# read_only=True,
# )
with open("/home/zuppif/Documents/Work/ActiveLoop/ai-shazam/data/lyrics_with_spotify_url_and_summary.json", "r") as f:
songs = json.load(f)
prompt = PromptTemplate(
input_variables=["songs", "user_input"],
template=Path("prompts/bot_with_summary.prompt").read_text(),
)
llm = ChatOpenAI(temperature=0.7)
chain = LLMChain(llm=llm, prompt=prompt)
return chain, songs, json.dumps(songs)
chain, songs, songs_json = init()
st.title("Disney song for you")
text_input = st.text_input(
label="How are you feeling today?",
placeholder="I am ready to rock and rool!",
)
clicked = st.button("Click me")
placeholder_emotions = st.empty()
placeholder = st.empty()
def get_emotions(user_input):
song_name = chain.run(songs=songs_json, user_input=user_input)
song_name.replace('\'', '')
print(f"Song: {song_name}")
song = songs[song_name.lower()]
# matches = db.similarity_search_with_score(emotions, distance_metric="cos")
# print(matches)
# doc, score = matches[0]
iframes_html = ""
# with placeholder_emotions:
# st.write(emotions)
with placeholder:
embed_url = song["embed_url"]
iframe_html = f'<iframe src="{embed_url}" style="border:0"> </iframe>'
st.components.v1.html(f"<div style='display:flex;flex-direction:column'>{iframe_html}</div>")
if clicked:
get_emotions(text_input) |