app.py

import gradio as gr
import requests
import time
import json
import os
import urllib.parse
import re
import base64
import mimetypes


def image_to_data_url(image_path):
    mimetypes.init()
    mime_type, _ = mimetypes.guess_type(image_path)
    if mime_type is None:
        mime_type = "image/unknown"
    if image_path.lower().endswith('.webp'):
        mime_type = "image/webp"
    with open(image_path, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
    data_url = f"data:{mime_type};base64,{encoded_string}"
    return data_url


def convert_url_filename(url):
    def encode_filename(match):
        full_filename = match.group(1)
        return urllib.parse.quote(full_filename)
    new_url = re.sub(r'/([^/]+)$', lambda m: '/' + encode_filename(m), url)
    return new_url


def process_image(image_path, prompt):    
    data_url = image_to_data_url(image_path)
    json_data = {
        'response_format': {
            'type': 'json_schema',
            'json_schema': {
                'name': 'image_critique',
                'strict': True,
                'schema': {
                    'type': 'object',
                    'properties': {
                        'critique': {
                            'type': 'string',
                        },
                    },
                    'required': [
                        'critique',
                    ],
                    'additionalProperties': False,
                },
            },
        },
        'chatSettings': {
            'model': 'gpt-4o-vision',
            'temperature': 0.7,
            'contextLength': 16385,
            'includeProfileContext': False,
            'includeWorkspaceInstructions': False,
            'embeddingsProvider': 'openai',
        },
        'messages': [
            {
                'role': 'user',
                'content': [
                    {
                        'type': 'image_url',
                        'image_url': {
                            'url': data_url
                        },
                    },
                    {
                        'type': 'text',
                        'text': "Напиши на русском языке промпт для создания похожего изображения",
                    },
                ],
            },
        ],
        'customModelId': '',
    }
    
    api_url = os.environ['api_url']
    print(api_url)
    response = requests.post(api_url, json=json_data)
    print("--->", response.status_code)
    print("--->", response.text)
    return response.json()['critique']


css = """
.gradio-container {
min-width: 100% !important;
}
#image_output {
height: 500px;
}
#generate {
    width: 100%;
    background: #e253dd !important;
    border: none;
    border-radius: 50px;
    outline: none !important;
    color: white;
}
#generate:hover {
    background: #de6bda !important;
    outline: none !important;
    color: #fff;
    }
"""


with gr.Blocks(css=css) as demo:
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(show_download_button=False, interactive=True, label='Изображение:', elem_id='image_output', type='filepath')
            text_button = gr.Button("Запустить нейросеть", variant='primary', elem_id="generate")
        with gr.Column():
            
            output = gr.Textbox(placeholder="", show_label=True, interactive=False, label='Результат:', lines=7)
            text_button.click(process_image, inputs=[image_input], outputs=output)

demo.queue(default_concurrency_limit=12)
demo.launch()