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import base64
import io
import cv2
import requests
import json
import gradio as gr
import os
from PIL import Image
import numpy as np
from PIL import ImageOps

# Accessing a specific environment variable
api_key = os.environ.get('devisionx')

# Checking if the environment variable exists
if not api_key:
    print("devisionx environment variable is not set.")
    exit()

# Define a function to call the API and get the results

def base64str_to_PILImage(base64str):
    base64_img_bytes = base64str.encode('utf-8')
    base64bytes = base64.b64decode(base64_img_bytes)
    bytesObj = io.BytesIO(base64bytes)
    return ImageOps.exif_transpose(Image.open(bytesObj))

def get_results(image, prompt):
    threshold = 0.5
    
    # Convert the NumPy array to PIL image
    image = Image.fromarray(image)

    # Convert the image to base64 string
    with io.BytesIO() as output:
        image.save(output, format="JPEG")
        base64str = base64.b64encode(output.getvalue()).decode("utf-8")

    # Prepare the payload (Adjust this part according to the API requirements)
    payload = json.dumps({"base64str": base64str, "classes": prompt})

    # Send the request to the API
    response = requests.put(api_key, data=payload)

    # Parse the JSON response
    data = response.json()
    print(response.status_code)
    print(data)

    # Access the values (Adjust this part according to the API response format)
    output_image_base64 = data['firstName']  # Assuming the API returns the output image as base64
    

    # Convert the output image from base64 to PIL and then to NumPy array
    output_image = base64str_to_PILImage(output_image_base64)
    output_image = np.array(output_image)

    return output_image

    
# Define the input components for Gradio (adding a new input for the prompt)
image_input = gr.inputs.Image()
text_input = gr.inputs.Textbox(label="Prompt")  # New input for the text prompt


# Define the output components for Gradio (including both image and text)
outputs = gr.Image(type="numpy", label="Output Image")

# Define the text description within an HTML <div> element
description_html = """
<html>
<head>
    <style>
        .description {
            margin: 20px;
            padding: 10px;
            border: 1px solid #ccc;
        }
    </style>
</head>
<body>
    <div class="description">
        <p><strong>Description:</strong></p>
        <p>We present a demo for performing object segmentation with training a Yolov8-seg on wheel Image dataset. The model was trained on 696 training images and validated on 199 images.</p>
        <p><strong>Usage:</strong></p>
        <p>You can upload wheel Image images, and the demo will provide you with your segmented image.</p>
        <p><strong>Dataset:</strong></p>
        <p>This dataset comprises a total of 994 images, which are divided into three distinct sets for various purposes:</p>
        <ul>
            <li><strong>Training Set:</strong> It includes 696 images and is intended for training the model.</li>
            <li><strong>Validation Set:</strong> There are 199 images in the validation set, which is used for optimizing model parameters during development.</li>
            <li><strong>Test Set:</strong> This set consists of 99 images and serves as a separate evaluation dataset to assess the performance of trained models.</li>
        </ul>
        <p><strong>License:</strong> This dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0).</p>
        <p>To access and download this dataset, please follow this link: <a href="https://universe.roboflow.com/project-wce7s/1000_seg_wheel" target="_blank">Dataset Download</a></p>
        <p><strong>Download Dataset:</strong></p>
        <p>To download the dataset we used, you can use the following command in colab:</p>
        <pre>!wget https://universe.roboflow.com/ds/OPPOJjnJPs?key=5yzDMD610e</pre>
        <p>Feel free to explore and use this repository for your object segmentation needs. If you have any questions or need assistance, please don't hesitate to reach out.</p>
    </div>
</body>
</html>
"""
title = "Brain Tumor Demo"


# Launch the Gradio interface with the description below it
gr.Interface(fn=get_results, inputs=[image_input, text_input], outputs=outputs,title=title, description=description_html).launch(share=False)