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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=API_KEY) | |
MODEL_ID = "gemini-2.0-flash-exp" # Replace with your desired model ID | |
def google_search_query(question): | |
try: | |
# Define the Google Search Tool | |
google_search_tool = Tool(google_search=GoogleSearch()) | |
# Generate the response | |
response = client.models.generate_content( | |
model=MODEL_ID, | |
contents=question, | |
config=GenerateContentConfig(tools=[google_search_tool]), | |
) | |
# 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="Google Search with Gemini AI", | |
description="Ask a question, and the AI will use Google search tools to provide an answer along with contextual search results.", | |
) | |
if __name__ == "__main__": | |
app.launch(share=True) |