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 clients
client_gemma = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")

def llava(inputs):
    """Processes an image and 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 based on text or image input."""
    if "files" in message and message["files"]:
        # Handle image + text input
        inputs = llava(message)
        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
            history[-1][1] = buffer  # Update the latest message in history
            yield history, history  # Yield both chatbot and history for updating

    else:
        # Handle text-only input
        user_message = message["text"]
        history.append([user_message, None])  # Add user's message with a placeholder response

        # Prepare prompt for the language model
        prompt = [{"role": "user", "content": msg[0]} for msg in history if msg[0]]
        response = client_gemma.chat_completion(prompt, max_tokens=200)

        # Extract response and update history
        bot_message = response["choices"][0]["message"]["content"]
        history[-1][1] = bot_message  # Update the latest message with bot's response
        yield history, history  # Yield both chatbot and history for updating

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

# Set up Gradio interface
with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    with gr.Row():
        with gr.Column():
            text_input = gr.Textbox(placeholder="Enter your message...")
            file_input = gr.File(label="Upload an image")

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

    def handle_file_upload(files, history=[]):
        """Handle file uploads and generate responses."""
        return respond({"files": files, "text": "Describe this image."}, history)

    # Connect components to callbacks
    text_input.submit(handle_text, [text_input, chatbot], [chatbot, chatbot])
    file_input.change(handle_file_upload, [file_input, chatbot], [chatbot, chatbot])

# Launch the Gradio app
demo.launch()