import gradio as gr
import requests
import os
import base64
from PIL import Image
import io

api_key = os.getenv('API_KEY')


def resize_image(image_path, max_size=(800, 800), quality=85):
    with Image.open(image_path) as img:
        img.thumbnail(max_size, Image.Resampling.LANCZOS)
        buffer = io.BytesIO()
        img.save(buffer, format="JPEG", quality=quality)
        return buffer.getvalue()

def filepath_to_base64(image_path):
    img_bytes = resize_image(image_path)
    img_base64 = base64.b64encode(img_bytes)
    return img_base64.decode('utf-8')

def format_response(response_body):
    content = response_body['choices'][0]['message']['content']
    formatted_content = content.replace("<0x0A>", "\n")
    return formatted_content

def call_deplot_api(image_path, content, temperature=0.2, top_p=0.7, max_tokens=1024):
    image_base64 = filepath_to_base64(image_path)
    invoke_url = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions/0bcd1a8c-451f-4b12-b7f0-64b4781190d1"
    api_key = os.getenv('API_KEY')
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Accept": "application/json",
    }
    payload = {
        "messages": [
            {
                "content": f"{content} <img src=\"data:image/jpeg;base64,{image_base64}\" />",
                "role": "user"
            }
        ],
        "temperature": temperature,
        "top_p": top_p,
        "max_tokens": max_tokens,
        "stream": False
    }
    session = requests.Session()
    response = session.post(invoke_url, headers=headers, json=payload)
    while response.status_code == 202:
        request_id = response.headers.get("NVCF-REQID")
        fetch_url = f"https://api.nvcf.nvidia.com/v2/nvcf/pexec/status/{request_id}"
        response = session.get(fetch_url, headers=headers)
    response.raise_for_status()
    response_body = response.json()
    return format_response(response_body)

content_input = gr.Textbox(lines=2, placeholder="Enter your content here...", label="Content")
image_input = gr.Image(type="filepath", label="Upload Image")
temperature_input = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.2, label="Temperature")
top_p_input = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.7, label="Top P")
max_tokens_input = gr.Slider(minimum=1, maximum=1024, step=1, value=1024, label="Max Tokens")

iface = gr.Interface(fn=call_deplot_api,
                     inputs=[image_input, content_input, temperature_input, top_p_input, max_tokens_input],
                     outputs="text",
                     title="Kosmos-2 API Explorer",
                     description="""
<div style="text-align: center; font-size: 1.5em; margin-bottom: 20px;">
<strong>Explore Visual Language Understanding with Kosmos-2</strong>
</div>
<p>
    Kosmos-2 model is a groundbreaking multimodal large language model (MLLM). Kosmos-2 is designed to ground text to the visual world, enabling it to understand and reason about visual elements in images.
</p>
"""
                    )

iface.launch()