import gradio as gr
from google import genai
import os

# Load API key from environment variable
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")

# Initialize the Google GenAI client
client = genai.Client(api_key=GEMINI_API_KEY)

# Function to interact with the Gemini API
def generate_content(prompt):
    # Generate content using the Gemini API
    response = client.models.generate_content(
        model="gemini-2.5-pro-exp-03-25",
        contents=[prompt]
    )
    
    # Format the result for better readability
    result_text = response.text.strip()  # Remove extra spaces and line breaks
    formatted_response = f"**AI Generated Content:**\n\n{result_text}"
    return formatted_response

# Create Gradio interface using Blocks for better control
with gr.Blocks() as interface:
    # Title and Description
    gr.Markdown("# Gemini AI Content Generator")
    gr.Markdown("Provide a prompt and get an explanation from Gemini AI.\n\nThe AI will return a well-formatted response.")
    
    # Text input field and submit button
    prompt_input = gr.Textbox(label="Enter your prompt:", placeholder="How does AI work?", lines=2)
    submit_button = gr.Button("Generate Content")
    
    # Output area for the result
    output_area = gr.Markdown()
    
    # Loading spinner (show loading during API call)
    loading_spinner = gr.HTML("<p style='color: #4CAF50;'>Loading... Please wait...</p>")

    # Define button click interaction
    def on_submit(prompt):
        # Show the spinner when the button is clicked
        return gr.update(visible=True), generate_content(prompt)

    # Link the button with the function
    submit_button.click(on_submit, inputs=prompt_input, outputs=[loading_spinner, output_area])

# Launch Gradio interface
interface.launch()