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. 🔍 THE WHY: Explain the underlying significance of this trend. " "3. 📈 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)