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

def chat_with_gemini(user_api_key, user_input, history):
    """
    Generates a response from the Gemini API based on user input and conversation history,
    using the provided user API key or falling back on a default API key.
    
    Args:
        user_api_key (str): The user's Google Gemini API key (optional).
        user_input (str): The latest message from the user.
        history (list): The conversation history as a list of message pairs.
    
    Returns:
        tuple: The updated conversation history with the chatbot's reply.
    """
    # Determine which API key to use
    api_key = user_api_key.strip() if user_api_key else os.getenv("YOUR_API_KEY")
    
    if not api_key:
        # If no API key is available, prompt the user
        history.append(["", "Please enter your Google Gemini API key to start the conversation."])
        return history, history
    
    try:
        # Configure the Gemini API with the selected API key
        genai.configure(api_key=api_key)
        
        # Initialize the Gemini Generative Model
        model = genai.GenerativeModel("gemini-1.5-flash")
        
        # Generate a response from the Gemini API
        response = model.generate_content(
            user_input,
            generation_config=genai.GenerationConfig(
                max_output_tokens=2000,
                temperature=0.7
            )
        )
        
        chatbot_reply = response.text.strip()
        
        # Append the user input and chatbot reply to the history as a single entry
        history.append([user_input, chatbot_reply])
        
        return history, history
    except Exception as e:
        error_message = f"An error occurred: {e}"
        history.append(["", error_message])
        return history, history

with gr.Blocks() as iface:
    gr.Markdown("""
    # Google Gemini Flash 1.5 Chatbot
    
    Welcome to the Google Gemini-powered chatbot! You can interact with the bot by typing your messages below.
    
    **API Key Setup:**
    - **Option 1:** Enter your own Google Gemini API key in the input field below.
    - **Option 2:** If you leave the API key field empty, the chatbot will use a default API key.
    
    > **Note:** Ensure that your API key is kept secure and do not share it publicly.
    """)
    
    with gr.Column():
        # API Key Input Section
        with gr.Row():
            api_key_input = gr.Textbox(
                label="Google Gemini API Key (Optional)",
                placeholder="Enter your API key here...",
                type="password",
                lines=1
            )
        
        # Chatbot Display
        chatbot = gr.Chatbot()
        
        # User Input Row
        with gr.Row():
            user_input = gr.Textbox(
                placeholder="Type your message here...",
                show_label=False
            )
            send_button = gr.Button("Send")
    
    # State to hold the conversation history
    history = gr.State([])
    
    def respond(user_api_key, message, history_state):
        """
        Handles the user message, generates a response using the provided or default API key,
        and updates the conversation history.
        
        Args:
            user_api_key (str): The user's API key (optional).
            message (str): The user's message.
            history_state (list): The current conversation history.
        
        Returns:
            tuple: Updated conversation history for display.
        """
        updated_history, new_history = chat_with_gemini(user_api_key, message, history_state)
        return updated_history, new_history
    
    # Connect the send button and textbox submission to the respond function
    send_button.click(respond, inputs=[api_key_input, user_input, history], outputs=[chatbot, history])
    user_input.submit(respond, inputs=[api_key_input, user_input, history], outputs=[chatbot, history])

if __name__ == "__main__":
    iface.launch()