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import gradio as gr | |
from gradio_client import Client | |
import os | |
import logging | |
import sqlite3 | |
import requests | |
from urllib.parse import urlparse, parse_qs | |
# Initialize the client for image generation | |
client_image = Client("mukaist/DALLE-4K") | |
# Define resolutions | |
resolutions = { | |
"896x1152": (896, 1152), | |
"1024x1024": (1024, 1024), | |
"1216x832": (1216, 832) | |
} | |
# Define the default style | |
DEFAULT_STYLE = "3840 x 2160" | |
# Set up logging | |
logging.basicConfig(level=logging.INFO) | |
logger = logging.getLogger(__name__) | |
# Database path | |
db_path = 'C:/Users/Administrator/Desktop/TGBoot/photo2prompt_db/users.db' | |
def generate_image(prompt, resolution_key, style=DEFAULT_STYLE): | |
resolution = resolutions.get(resolution_key, (1024, 1024)) | |
width, height = resolution | |
full_prompt = f"{prompt}, Canon EOS R5, 4K, Photo-Realistic, appearing photorealistic with super fine details, high resolution, natural look, hyper realistic photography, cinematic lighting, --ar 64:37, --v 6.0, --style raw, --stylize 750" | |
try: | |
result = client_image.predict( | |
prompt=full_prompt, | |
negative_prompt="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation", | |
use_negative_prompt=True, | |
style=style, | |
seed=0, | |
width=width, | |
height=height, | |
guidance_scale=5, | |
randomize_seed=True, | |
api_name="/run" | |
) | |
logger.info("Image generation successful.") | |
return result | |
except Exception as e: | |
logger.error(f"Error generating image: {e}") | |
return None | |
def update_user_points(user_chat_id, points): | |
"""Updates the user's points in the database.""" | |
with sqlite3.connect(db_path) as conn: | |
cursor = conn.cursor() | |
cursor.execute(''' | |
INSERT INTO users (user_chat_id, points) VALUES (?, ?) | |
ON CONFLICT(user_chat_id) DO UPDATE SET points = points + ? | |
''', (user_chat_id, points, points)) | |
conn.commit() | |
logger.info(f"User with ID {user_chat_id} had their points updated by {points}.") | |
def notify_webhook(user_chat_id): | |
"""Notify the webhook server about the points update.""" | |
webhook_url = "http://65.108.76.174:5000/update_points" | |
payload = {'user_chat_id': user_chat_id, 'points': 10} # Adjust payload as necessary | |
try: | |
response = requests.post(webhook_url, json=payload) | |
response.raise_for_status() | |
logger.info(f"Webhook notification sent successfully. Response: {response.status_code}") | |
except requests.RequestException as e: | |
logger.error(f"Error sending webhook notification: {e}") | |
def get_user_points(user_chat_id): | |
"""Retrieve user points from the Flask server.""" | |
webhook_url = "http://65.108.76.174:5000/get_points" | |
params = {'user_chat_id': user_chat_id} | |
try: | |
response = requests.get(webhook_url, params=params) | |
response.raise_for_status() | |
return response.json().get('points', "Error retrieving points") | |
except requests.RequestException as e: | |
logger.error(f"Error fetching user points: {e}") | |
return "Failed to retrieve user points" | |
def extract_user_chat_id_from_url(url): | |
parsed_url = urlparse(url) | |
params = parse_qs(parsed_url.query) | |
return params.get('user_chat_id', ["Guest"])[0] | |
def gradio_interface(prompt, resolution_key, user_chat_id): | |
result = generate_image(prompt, resolution_key) | |
if result and result[0]: | |
file_path = result[0][0].get('image') | |
if file_path and os.path.exists(file_path): | |
update_user_points(user_chat_id, 10) # Award 10 points for each image generated | |
notify_webhook(user_chat_id) # Notify the webhook server | |
return file_path, "The image was generated successfully." | |
else: | |
return None, "The image file is not available. Please try again later." | |
else: | |
return None, "There was an error processing your photo. Please try again later." | |
def create_gradio_interface(): | |
with gr.Blocks() as interface: | |
# Personalized HTML content | |
gr.HTML(""" | |
<script src="https://telegram.org/js/telegram-web-app.js"></script> | |
<script> | |
document.addEventListener('DOMContentLoaded', function() { | |
window.Telegram.WebApp.init(); | |
const urlParams = new URLSearchParams(window.location.search); | |
const userChatId = urlParams.get('user_chat_id') || "Guest"; | |
window.Telegram.WebApp.setTitle(`Welcome!`); | |
document.getElementById('user-chat-id-input').value = userChatId; // Set hidden textbox with userChatId | |
}); | |
</script> | |
<h3>Welcome! Generate images based on prompts.</h3> | |
""") | |
# Create input components | |
prompt_input = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...") | |
resolution_dropdown = gr.Dropdown(choices=list(resolutions.keys()), label="Resolution", value="1024x1024") | |
generate_button = gr.Button("Generate") | |
# Create output components | |
result_output = gr.Image(label="Generated Image", type="pil") | |
message_output = gr.Textbox(label="Result", placeholder="Results will be shown here", interactive=False) | |
# Button to get user points | |
get_points_button = gr.Button("Get Points") | |
points_output = gr.Textbox(label="User Points", placeholder="User points will be displayed here", interactive=False) | |
# Hidden textbox to hold the user_chat_id | |
user_chat_id_input = gr.Textbox(visible=False, value="Guest", elem_id="user-chat-id-input") | |
# Set up interaction | |
generate_button.click( | |
fn=lambda prompt, resolution_key, user_chat_id: gradio_interface(prompt, resolution_key, user_chat_id), | |
inputs=[prompt_input, resolution_dropdown, user_chat_id_input], | |
outputs=[result_output, message_output] | |
) | |
get_points_button.click( | |
fn=lambda user_chat_id: get_user_points(user_chat_id), | |
inputs=[user_chat_id_input], | |
outputs=points_output | |
) | |
gr.HTML(""" | |
<style> | |
footer.svelte-1rjryqp { | |
display: none !important; | |
} | |
</style> | |
""") | |
return interface | |
def launch_gradio(): | |
interface = create_gradio_interface() | |
interface.launch() | |
if __name__ == '__main__': | |
# Launch Gradio | |
launch_gradio() |