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File size: 2,849 Bytes
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import streamlit as st
from sentence_transformers import SentenceTransformer
import weaviate
import base64
import requests
bg_color = "#343434" # Set your desired background color
st.markdown(
f"""
<style>
body {{
background-color: {bg_color};
}}
</style>
""",
unsafe_allow_html=True,
)
st.sidebar.header("Try your query on the following: ")
topics = ["Character", "Compassion", "Culture", "Devotion", "Dharma", "Discipline", "Education", "Festivals",
"Gayatri Devi", "God", "Gratitude", "Guru", "Human Values", "India", "Love", "Meditation", "Mind",
"Money", "Namasmarana", "Parents", "Pleasure and Pain", "Practice", "Sacrifice", "Sadhana", "Senses",
"Service", "Time"]
for topic in topics:
st.sidebar.write(topic)
# Set up the layout using columns
col1, col2 = st.columns([2, 1])
# Left column: Title, Search Bar, and Search Button
with col1:
st.header('Harmony Hub: Connecting with Spiritual Messages for Inner Peace')
query_text = st.text_input('Enter your query:')
search_button = st.button('Search')
# Display the search results
if search_button:
try:
# Encode the query text
embedder = SentenceTransformer('all-MiniLM-L6-v2')
query_embeddings = embedder.encode([query_text], convert_to_tensor=True)
query_vector = query_embeddings.flatten().tolist()
# Perform Weaviate search
client = weaviate.connect_to_embedded(
persistence_data_path='Transcript Data'
)
info = client.collections.get("Audio")
query_vector = query_vector
response = info.query.near_vector(
near_vector=query_vector,
limit=3,
return_properties=["audio", "transcript"]
)
result = {'audio': '', 'transcript': ''}
if response.objects:
obj = response.objects[0]
base64_audio = obj.properties.get("audio", "")
transcript = obj.properties.get("transcript", "")
if base64_audio:
decoded_audio = base64.b64decode(base64_audio.encode("utf-8"))
audio_data = base64.b64encode(decoded_audio).decode("utf-8")
result['audio'] = audio_data
result['transcript'] = transcript
# Display audio player and transcript
st.audio(base64.b64decode(result['audio']), format='audio/mp3')
st.write('Transcript:', result['transcript'])
except Exception as e:
st.write('Error:', str(e))
# Right column: Image
with col2:
image_path = 'Swami1.jpeg'
st.image(image_path, caption='Conscience is our real power, strength, and awareness.', use_column_width=True) |