import gradio as gr
from huggingface_hub import InferenceClient
from transformers import LlavaProcessor, LlavaForConditionalGeneration, TextIteratorStreamer
from PIL import Image
from threading import Thread

# Initialize model and processor
model_id = "llava-hf/llava-interleave-qwen-0.5b-hf"
processor = LlavaProcessor.from_pretrained(model_id)
model = LlavaForConditionalGeneration.from_pretrained(model_id).to("cpu")

# Initialize inference client
client_gemma = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")

# Functions
def llava(inputs, history):
    """Processes image + text input using Llava."""
    image = Image.open(inputs["files"][0]).convert("RGB")
    prompt = f"<|im_start|>user <image>\n{inputs['text']}<|im_end|>"
    processed = processor(prompt, image, return_tensors="pt").to("cpu")
    return processed

def respond(message, history):
    """Generate a response for text or image input."""
    if "files" in message and message["files"]:
        # Handle image + text input
        inputs = llava(message, history)
        streamer = TextIteratorStreamer(skip_prompt=True, skip_special_tokens=True)
        thread = Thread(target=model.generate, kwargs=dict(inputs=inputs, max_new_tokens=512, streamer=streamer))
        thread.start()
        buffer = ""
        for new_text in streamer:
            buffer += new_text
            yield buffer
    else:
        # Handle text input
        user_message = message["text"]
        history.append([user_message, None])  # Append user message to history
        prompt = [{"role": "user", "content": msg[0]} for msg in history if msg[0]]
        response = client_gemma.chat_completion(prompt, max_tokens=200)
        bot_message = response["choices"][0]["message"]["content"]
        history[-1][1] = bot_message  # Update history with bot's response
        yield history

def generate_image(prompt):
    """Generates an image based on user prompt."""
    client = InferenceClient("KingNish/Image-Gen-Pro")
    return client.predict("Image Generation", None, prompt, api_name="/image_gen_pro")

# Gradio app setup with multi-page and sidebar
with gr.Blocks(title="AI Chat & Tools", theme="compact") as demo:
    with gr.Sidebar():
        gr.Markdown("## AI Assistant Sidebar")
        gr.Markdown("Navigate through features and try them out.")
        gr.Button("Open Chat").click(None, [], [], _js="() => window.location.hash='#chat'")
        gr.Button("Generate Image").click(None, [], [], _js="() => window.location.hash='#image'")

    with gr.Page("chat", title="Chat Interface"):
        chatbot = gr.Chatbot(label="Chat with AI Assistant", show_label=False)
        with gr.Row():
            text_input = gr.Textbox(placeholder="Enter your message...", lines=2, show_label=False)
            file_input = gr.File(label="Upload an image", file_types=["image/*"])

        def handle_text(text, history=[]):
            """Handle text input."""
            return respond({"text": text}, history), history

        def handle_file(files, history=[]):
            """Handle file upload."""
            return respond({"files": files, "text": "Describe this image."}, history), history

        # Connect callbacks
        text_input.submit(handle_text, [text_input, chatbot], [chatbot])
        file_input.change(handle_file, [file_input, chatbot], [chatbot])

    with gr.Page("image", title="Generate Image"):
        gr.Markdown("### Image Generator")
        image_prompt = gr.Textbox(placeholder="Describe the image to generate", show_label=False)
        image_output = gr.Image(label="Generated Image")

        def generate_image_callback(prompt):
            """Handle image generation."""
            return generate_image(prompt)

        image_prompt.submit(generate_image_callback, [image_prompt], [image_output])

# Launch Gradio app
demo.launch()