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bbda6f5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | import gradio as gr
from smolagents import CodeAgent, HfApiClient, DuckDuckGoSearchTool
# Initialize search capability
search_tool = DuckDuckGoSearchTool()
# Set up Zquery1 agent with a highly optimized model for search task handling
agent = CodeAgent(
tools=[search_tool],
model=HfApiClient("Qwen/Qwen2.5-72B-Instruct"),
system_prompt=(
"You are Zquery1, an advanced AI search engine. "
"Your task is to search the web for the user's query, extract relevant facts, "
"and synthesize a clean response. Always include clickable source links "
"using markdown format: [Site Name](URL). Never use raw URLs."
)
)
def respond(message, history):
try:
# Run agent search pipeline
bot_response = agent.run(message)
return str(bot_response)
except Exception as e:
return f"An error occurred while fetching live data: {str(e)}"
# Custom CSS styling to make the embedded UI sleek and seamless
custom_css = """
footer {visibility: hidden}
.gradio-container {background-color: transparent !important;}
"""
# Build interface configured for embedding
with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
gr.Markdown("# <center>🔍 Zquery1 Web Search Engine</center>")
gr.ChatInterface(
fn=respond,
examples=[
"What are the top space discoveries this week?",
"Latest news about open source AI models",
"What is the current stock price of Apple?"
],
cache_examples=False
)
if __name__ == "__main__":
demo.launch()
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