import gradio as gr
import google.generativeai as genai
import os

# Setup
GOOGLE_AI_STUDIO = os.environ.get('GOOGLE_AI_STUDIO2')
genai.configure(api_key=GOOGLE_AI_STUDIO)

# Model initialization
model = genai.GenerativeModel('gemini-pro')

def get_response(query):
    """
    Searches for content based on the provided query using the Gemini model.
    Handles DeadlineExceeded exceptions from the Google API.
    Args:
        query (str): The search query.
    Returns:
        str: The response text from the Gemini model or an error message.
    """
    try:
        response = model.generate_content(query)
        return response.text
    except exceptions.DeadlineExceeded as e:
        # Handle the DeadlineExceeded exception here
        print("Error: Deadline Exceeded -", str(e))
        # You can return a custom message or take other appropriate actions
        return "Error: The request timed out. Please try again later."


# Gradio interface
iface = gr.Interface(
    fn=get_response,
    inputs=gr.Textbox(label="Enter your query"),
    outputs=gr.Textbox(label="Response"),
    title="AI Content Generator",
    description="Enter a query to generate content using Gemini Pro model."
)

iface.launch()