import os
import time
from typing import List, Tuple, Optional

import google.generativeai as genai
import gradio as gr
from PIL import Image

# Ensure Google API Key is set
GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")

TITLE = """<h1 align="center">☕ Espresso with LeProf 🔥</h1>"""
SUBTITLE = """<h2 align="center">🌟 Knowledge Shots for Curious Minds</h2>"""

IMAGE_WIDTH = 512

def preprocess_stop_sequences(stop_sequences: str) -> Optional[List[str]]:
    return [seq.strip() for seq in stop_sequences.split(",")] if stop_sequences else None

def preprocess_image(image: Image.Image) -> Image.Image:
    image_height = int(image.height * IMAGE_WIDTH / image.width)
    return image.resize((IMAGE_WIDTH, image_height))

def user(text_prompt: str, chatbot: List[Tuple[str, str]]):
    """Handles user input and appends to the chatbot."""
    return "", chatbot + [[text_prompt, None]]

def bot(
    google_key: str,
    image_prompt: Optional[Image.Image],
    temperature: float,
    max_output_tokens: int,
    stop_sequences: str,
    top_k: int,
    top_p: float,
    topic: str,
    chatbot: List[Tuple[str, str]],
):
    """Generates a response using Google Gemini."""
    google_key = google_key or GOOGLE_API_KEY
    if not google_key:
        raise ValueError("GOOGLE_API_KEY is not set. Please set it up.")
    if not topic:
        raise ValueError("Topic is not set. Please provide a topic.")

    # Get the user's input
    text_prompt = chatbot[-1][0]

    # Construct the system prompt
    analysis_system_prompt = (
        f"You are an expert in {topic}. Analyze the provided text with a focus on {topic}, "
        "identifying recent issues, insights, or improvements relevant to academic standards and effectiveness. "
        "Offer actionable advice for enhancing knowledge and provide real-life examples."
    )

    # Configure Generative AI model
    genai.configure(api_key=google_key)
    generation_config = genai.types.GenerationConfig(
        temperature=temperature,
        max_output_tokens=max_output_tokens,
        stop_sequences=preprocess_stop_sequences(stop_sequences),
        top_k=top_k,
        top_p=top_p,
    )

    model_name = "gemini-1.5-pro-latest"
    model = genai.GenerativeModel(model_name)

    try:
        # Pass the system prompt and user input as a single prompt
        response = model.generate_content(
            prompt=analysis_system_prompt,
            input=text_prompt,
            generation_config=generation_config,
        )
    except KeyError as e:
        raise KeyError(f"Error in response generation: {e}")

    # Process and stream the response
    chatbot[-1][1] = ""
    for chunk in response:
        for i in range(0, len(chunk.text), 10):
            chatbot[-1][1] += chunk.text[i : i + 10]
            time.sleep(0.01)
            yield chatbot

# Gradio Components
google_key_component = gr.Textbox(
    label="GOOGLE API KEY",
    type="password",
    placeholder="Enter your API key...",
    visible=GOOGLE_API_KEY is None,
)

topic_input = gr.Textbox(label="Set the Topic", placeholder="e.g., AI in Education, Human-Computer Interaction")
text_prompt_component = gr.Textbox(label="Ask LeProf", placeholder="Type your question here...")
chatbot_component = gr.Chatbot(label="LeProf says")
run_button_component = gr.Button("🫗 Get Your Knowledge Shot")

example_data = [
    ["AI in Education", "What are the challenges in AI tools for personalized learning?"],
    ["Multimedia Accessibility", "How can multimedia be made more accessible to people with disabilities?"],
    ["Ethical AI", "What are the ethical implications of AI in social media content moderation?"],
    ["Virtual Reality", "How does virtual reality improve skill training in industries?"],
    ["Augmented Reality", "What are the UX challenges in augmented reality for urban navigation?"],
]

# Advanced Settings
temperature_component = gr.Slider(
    minimum=0, maximum=1.0, value=0.4, step=0.05, label="Creativity Level"
)
max_output_tokens_component = gr.Slider(
    minimum=1, maximum=2048, value=1024, step=1, label="Max Tokens"
)
stop_sequences_component = gr.Textbox(label="Stop Sequences", placeholder="e.g., STOP, END")
top_k_component = gr.Slider(
    minimum=1, maximum=40, value=32, step=1, label="Top-K Sampling"
)
top_p_component = gr.Slider(
    minimum=0, maximum=1.0, value=1.0, step=0.01, label="Top-P Sampling"
)

# Layout with Gradio Blocks
with gr.Blocks() as demo:
    gr.HTML(TITLE)
    gr.HTML(SUBTITLE)
    google_key_component.render()
    topic_input.render()
    chatbot_component.render()
    text_prompt_component.render()
    gr.Examples(
        examples=example_data,
        inputs=[topic_input, text_prompt_component],
        label="Example Questions",
    )
    run_button_component.render()
    with gr.Accordion("Parameters", open=False):
        temperature_component.render()
        max_output_tokens_component.render()
        stop_sequences_component.render()
        with gr.Accordion("Advanced Settings", open=False):
            top_k_component.render()
            top_p_component.render()

    # Event Handlers
    run_button_component.click(
        fn=user, inputs=[text_prompt_component, chatbot_component], outputs=[text_prompt_component, chatbot_component], queue=False
    ).then(
        fn=bot,
        inputs=[
            google_key_component,
            None,  # Placeholder for image_prompt (not used in this example)
            temperature_component,
            max_output_tokens_component,
            stop_sequences_component,
            top_k_component,
            top_p_component,
            topic_input,
            chatbot_component,
        ],
        outputs=[chatbot_component]
    )
    text_prompt_component.submit(
        fn=user, inputs=[text_prompt_component, chatbot_component], outputs=[text_prompt_component, chatbot_component], queue=False
    ).then(
        fn=bot,
        inputs=[
            google_key_component,
            None,  # Placeholder for image_prompt (not used in this example)
            temperature_component,
            max_output_tokens_component,
            stop_sequences_component,
            top_k_component,
            top_p_component,
            topic_input,
            chatbot_component,
        ],
        outputs=[chatbot_component]
    )

# Launch the App
demo.launch()