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Update app.py
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import os
import gradio as gr
from google import genai
from google.genai.types import GenerateContentConfig, GoogleSearch, Tool
# Initialize GenAI Client
API_KEY = os.getenv("GOOGLE_API_KEY") # Ensure to set this in Hugging Face Secrets
client = genai.Client(api_key="AIzaSyCO-ynPjcqQ17ZkTD2i0dm0XEjmRIxGp0k")
MODEL_ID = "gemini-2.5-flash" # Replace with your desired model ID
def google_search_query(question):
try:
# Define the Google Search Tool
google_search_tool = Tool(google_search=GoogleSearch())
instruction = (
"You are 'TrendScout AI', a sophisticated strategic analyst. "
"Your goal is to synthesize real-time web data into actionable insights. "
"When a user asks a question, follow this structure: "
"1. ⚑ THE SIGNAL: A concise summary of the most recent development. "
"2. πŸ” THE WHY: Explain the underlying significance of this trend. "
"3. πŸ“ˆ THE VERDICT: A strategic prediction for the next 6-12 months. "
"Keep the tone professional and use bullet points for readability."
)
# Generate the response
response = client.models.generate_content(
model=MODEL_ID,
contents=question,
config=GenerateContentConfig(tools=[google_search_tool],system_instruction=instruction),
)
# Extract AI response and search results
ai_response = response.text # AI response as plain text
search_results = response.candidates[0].grounding_metadata.search_entry_point.rendered_content
return ai_response, search_results
except Exception as e:
return f"Error: {str(e)}", ""
# Gradio Interface
app = gr.Interface(
fn=google_search_query,
inputs=gr.Textbox(lines=2, label="Ask a Question"),
outputs=[
gr.Textbox(label="AI Response"),
gr.HTML(label="Search Results"),
],
title="🌐 TrendScout AI",
description="Stop searching. Start scouting. This AI analyzes live Google data to identify market shifts and future signals before they go mainstream.",
)
if __name__ == "__main__":
app.launch(share=True)