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import gradio as gr
from product_recommender import DynamicRecommender
import asyncio
async def get_recommendations(text: str) -> str:
"""
Return the final recommendations as a markdown string
with product name, price, link, etc.
"""
try:
recommender = DynamicRecommender()
results = await recommender.get_recommendations(text)
if not results:
return "No recommendations found. Possibly the scraping returned nothing."
# Build a markdown output
# Each product: name, price, link
# e.g. "**Name**: iPhone 14\n**Price**: 80,000\n[Open Link](https://...)"
lines = []
for i, product in enumerate(results, start=1):
name = product.get("name", "Unknown")
price = product.get("price", "N/A")
url = product.get("url", "#")
source = product.get("source", "")
# Markdown formatting
lines.append(
f"**{i}. {name}**\n\n"
f"- **Price**: {price}\n"
f"- **Source**: {source}\n"
f"- **Link**: [View here]({url})\n"
f"---"
)
markdown_output = "\n".join(lines)
return markdown_output
except Exception as e:
return f"Error: {str(e)}"
demo = gr.Interface(
fn=lambda x: asyncio.run(get_recommendations(x)),
inputs=gr.Textbox(
lines=3,
placeholder="Describe who you're buying a gift for (age, interests, etc.)"
),
outputs=gr.Markdown(), # or gr.HTML() if you prefer
title="🎁 Smart Gift Recommender",
description="Get personalized gift suggestions with real-time price comparison!\n\nType something like: 'I need a thoughtful and creative gift for a 25-year-old who loves art...'"
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860)
else:
app = demo.app |