import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor, TextIteratorStreamer
import gradio as gr
from threading import Thread
from PIL import Image

# Constants
TITLE = "<h1><center>Phi 3.5 Multimodal (Text + Vision)</center></h1>"
DESCRIPTION = "# Phi-3.5 Multimodal Demo (Text + Vision)"

# Model configurations
TEXT_MODEL_ID = "microsoft/Phi-3.5-mini-instruct"
VISION_MODEL_ID = "microsoft/Phi-3.5-vision-instruct"

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

# Load models and tokenizers
text_tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_ID)
text_model = AutoModelForCausalLM.from_pretrained(
    TEXT_MODEL_ID,
    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
    device_map="auto",
    low_cpu_mem_usage=True
)

vision_model = AutoModelForCausalLM.from_pretrained(
    VISION_MODEL_ID, 
    trust_remote_code=True, 
    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
    attn_implementation="flash_attention_2" if device == "cuda" else None,
    low_cpu_mem_usage=True
).to(device).eval()

vision_processor = AutoProcessor.from_pretrained(VISION_MODEL_ID, trust_remote_code=True)

# Helper functions
def stream_text_chat(message, history, system_prompt, temperature=0.8, max_new_tokens=1024, top_p=1.0, top_k=20):
    conversation = [{"role": "system", "content": system_prompt}]
    for prompt, answer in history:
        conversation.extend([
            {"role": "user", "content": prompt},
            {"role": "assistant", "content": answer},
        ])
    conversation.append({"role": "user", "content": message})

    input_ids = text_tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(device)
    streamer = TextIteratorStreamer(text_tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        input_ids=input_ids,
        max_new_tokens=max_new_tokens,
        do_sample=temperature > 0,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        eos_token_id=[128001, 128008, 128009],
        streamer=streamer,
    )

    with torch.no_grad():
        thread = Thread(target=text_model.generate, kwargs=generate_kwargs)
        thread.start()

    buffer = ""
    for new_text in streamer:
        buffer += new_text
        yield buffer

def process_vision_query(image, text_input):
    prompt = f"<|user|>\n<|image_1|>\n{text_input}<|end|>\n<|assistant|>\n"
    image = Image.fromarray(image).convert("RGB")
    inputs = vision_processor(prompt, image, return_tensors="pt").to(device)
    
    with torch.no_grad():
        generate_ids = vision_model.generate(
            **inputs, 
            max_new_tokens=1000, 
            eos_token_id=vision_processor.tokenizer.eos_token_id
        )
    
    generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
    response = vision_processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    return response

# Gradio interface
with gr.Blocks() as demo:
    gr.HTML(TITLE)
    gr.Markdown(DESCRIPTION)

    with gr.Tab("Text Model (Phi-3.5-mini)"):
        chatbot = gr.Chatbot(height=600)
        gr.ChatInterface(
            fn=stream_text_chat,
            chatbot=chatbot,
            additional_inputs=[
                gr.Textbox(value="You are a helpful assistant", label="System Prompt"),
                gr.Slider(minimum=0, maximum=1, step=0.1, value=0.8, label="Temperature"),
                gr.Slider(minimum=128, maximum=8192, step=1, value=1024, label="Max new tokens"),
                gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="top_p"),
                gr.Slider(minimum=1, maximum=20, step=1, value=20, label="top_k"),
            ],
        )

    with gr.Tab("Vision Model (Phi-3.5-vision)"):
        with gr.Row():
            with gr.Column():
                vision_input_img = gr.Image(label="Input Picture")
                vision_text_input = gr.Textbox(label="Question")
                vision_submit_btn = gr.Button(value="Submit")
            with gr.Column():
                vision_output_text = gr.Textbox(label="Output Text")
        
        vision_submit_btn.click(process_vision_query, [vision_input_img, vision_text_input], [vision_output_text])

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