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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 = "autoannotation"
description = "This is a project description. It demonstrates how to use Gradio with an image and text input to interact with an API."
import os
examples = [
["3000.jpeg",'person,car,traffic sign,traffic light'],
["original (8).jpg", 'person,car,traffic sign,traffic light'],
["traffic.jpg", 'person,car,traffic sign,traffic light'],
]
# Create a Blocks object and use it as a context manager
with gr.Blocks() as demo:
gr.Markdown(
"""
# Tuba Autoannotation Demo
This is your private demo for [Tuba Autoannotation](https://Tuba.ai)
A prompt based controllable model for auto annotation
"""
)
# Define the input components and add them to the layout
with gr.Row():
image_input = gr.inputs.Image()
output = gr.Image(type="numpy", label="Output Image")
# Define the output component and add it to the layout
with gr.Row():
text_input = gr.inputs.Textbox(label="Prompt")
with gr.Row():
button = gr.Button("Run")
# Define the event listener that connects the input and output components and triggers the function
button.click(fn=get_results, inputs=[image_input, text_input], outputs=output, api_name="get_results")
# Add the description below the layout
gr.Examples(
fn=get_results,
examples=examples,
inputs=[image_input, text_input],
outputs=[outputs]
)
gr.Markdown(description_html)
# Launch the app
demo.launch(share=False) |