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"""
Semantic Quote Search Engine
AIPI 510 - Deployed on Hugging Face Spaces, Jaideep
"""
import gradio as gr
from sentence_transformers import SentenceTransformer
import chromadb
import os
# intialization
# Load embedding model
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
print("Model loaded!")
# Load existing ChromaDB (pre-built, not created on the fly)
chroma_path = "./chromadb"
client = chromadb.PersistentClient(path=chroma_path)
collection = client.get_collection("quotes_collection")
print(f"Loaded collection with {collection.count()} documents!")
# SEARCH FUNCTION
def semantic_search(query, n_results=5):
"""
Perform semantic search over the quotes collection.
"""
# Encode query using the same model
query_embedding = model.encode([query])
# Query ChromaDB for similar documents
results = collection.query(
query_embeddings=query_embedding.tolist(),
n_results=n_results,
include=['documents', 'metadatas', 'distances']
)
# Format results nicely
output = []
for i in range(len(results['documents'][0])):
meta = results['metadatas'][0][i]
distance = results['distances'][0][i]
similarity = 1 - (distance / 2) # Convert distance to similarity score
result_text = f"""
### Result {i+1} (Similarity: {similarity:.1%})
> "{meta['quote']}"
**— {meta['author']}**
🏷️ *Tags: {meta['tags']}*
"""
output.append(result_text)
return "\n---\n".join(output)
def search_quotes(query, num_results):
"""Wrapper function for Gradio interface"""
if not query.strip():
return "Please enter a search query!"
return semantic_search(query, n_results=int(num_results))
# gradio interface
demo = gr.Interface(
fn=search_quotes,
inputs=[
gr.Textbox(
label="🔍 Search Query",
placeholder="Try: 'love', 'success', 'wisdom', 'courage'...",
lines=2
),
gr.Slider(
minimum=1,
maximum=10,
value=5,
step=1,
label=" Number of Results"
)
],
outputs=gr.Markdown(label=" Search Results"),
title=" Semantic Quote Search Engine",
description="""
## Search through famous quotes using AI-powered semantic similarity!
Unlike traditional keyword search, this understands the **meaning** of your query.
**How it works:**
1. Your query is converted to a vector using a transformer model
2. We find quotes with the most similar meaning in our database
3. Results are ranked by semantic similarity
*Built for AIPI 510: Data Sourcing for Analytics | Duke University*
""",
examples=[
["finding happiness in life", 5],
["overcoming fear and challenges", 5],
["the importance of friendship", 3],
["learning from mistakes", 5],
["believing in yourself", 3]
]
)
# Launch the app
if __name__ == "__main__":
demo.launch()
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