app.py

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")

# Launch the Gradio interface
gr.Interface(fn=get_results, inputs=[image_input, text_input], outputs=outputs).launch(share=False)