main.py

from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
import gradio as gr
import os
import sys
import random
import string
import time
from queue import Queue
from threading import Thread
import requests
import io
from PIL import Image
import base64
from deep_translator import GoogleTranslator

app = FastAPI()

API_URL = "https://api-inference.huggingface.co/models/playgroundai/playground-v2-1024px-aesthetic"
API_TOKEN = os.getenv("HF_READ_TOKEN") # it is free
headers = {"Authorization": f"Bearer {API_TOKEN}"}





text_gen = gr.Interface.load("models/Gustavosta/MagicPrompt-Stable-Diffusion")
proc1 = gr.Interface.load("models/playgroundai/playground-v2-1024px-aesthetic")

queue = Queue()
queue_threshold = 100

def add_random_noise(prompt, noise_level=0.00):
    if noise_level == 0:
        noise_level = 0.00
    percentage_noise = noise_level * 5
    num_noise_chars = int(len(prompt) * (percentage_noise / 100))
    noise_indices = random.sample(range(len(prompt)), num_noise_chars)
    prompt_list = list(prompt)
    noise_chars = list(string.ascii_letters + string.punctuation + ' ' + string.digits)
    noise_chars.extend(['😍', '💩', '😂', '🤔', '😊', '🤗', '😭', '🙄', '😷', '🤯', '🤫', '🥴', '😴', '🤩', '🥳', '😔', '😩', '🤪', '😇', '🤢', '😈', '👹', '👻', '🤖', '👽', '💀', '🎃', '🎅', '🎄', '🎁', '🎂', '🎉', '🎈', '🎊', '🎮', '❤️', '💔', '💕', '💖', '💗', '🐶', '🐱', '🐭', '🐹', '🦊', '🐻', '🐨', '🐯', '🦁', '🐘', '🔥', '🌧️', '🌞', '🌈', '💥', '🌴', '🌊', '🌺', '🌻', '🌸', '🎨', '🌅', '🌌', '☁️', '⛈️', '❄️', '☀️', '🌤️', '⛅️', '🌥️', '🌦️', '🌧️', '🌩️', '🌨️', '🌫️', '☔️', '🌬️', '💨', '🌪️', '🌈'])
    for index in noise_indices:
        prompt_list[index] = random.choice(noise_chars)
    return "".join(prompt_list)

# Existing code...

import uuid  # Import the UUID library

# Existing code...

# Existing code...

request_counter = 0  # Global counter to track requests

def send_it1(inputs, noise_level, proc=proc1):
    global request_counter
    request_counter += 1
    timestamp = f"{time.time()}_{request_counter}"
    prompt_with_noise = add_random_noise(inputs, noise_level) + f" - {timestamp}"
    while queue.qsize() >= queue_threshold:
            time.sleep(2)
    queue.put(prompt_with_noise)
    output = proc(prompt_with_noise)
    return output
    
def generate_image(inputs, is_negative, steps, cfg_scale, seed):
    try:
        global request_counter
        request_counter += 1
        timestamp = f"{time.time()}_{request_counter}"
        
        # Translate inputs to English
        translator_to_en = GoogleTranslator(source='auto', target='english')
        english_inputs = translator_to_en.translate(inputs)
        
        prompt_with_noise = add_random_noise(english_inputs) + f" - {timestamp}"
        payload = {
            "inputs": prompt_with_noise,
            "is_negative": is_negative,
            "steps": steps,
            "cfg_scale": cfg_scale,
            "seed": seed if seed is not None else random.randint(-1, 2147483647)
        }
        response = requests.post(API_URL, headers=headers, json=payload)
        response.raise_for_status()  # Raise an exception for HTTP errors
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        return image
    except requests.exceptions.HTTPError as e:
        # Handle any HTTP errors
        print(f"HTTP Error: {e}")
        return "An unexpected error occurred while generating the image."
    except Exception as e:
        # Handle other exceptions
        print(f"Error generating image: {e}")
        return "An unexpected error occurred while generating the image. Please try again later."




def get_prompts(prompt_text):
    if not prompt_text:
        return "Please enter text before generating prompts.رجاء ادخل النص اولا" 
        raise gr.Error("Please enter text before generating prompts.رجاء ادخل النص اولا")
    else:
        global request_counter
        request_counter += 1
        timestamp = f"{time.time()}_{request_counter}"

    options = [
        "Cyberpunk android",
        "2060",
        "newyork",
        "style of laurie greasley" , "studio ghibli" , "akira toriyama" , "james gilleard" , "genshin impact" , "trending pixiv fanbox" , "acrylic palette knife, 4k, vibrant colors, devinart, trending on artstation, low details"
        "Editorial Photography, Shot on 70mm lens, Depth of Field, Bokeh, DOF, Tilt Blur, Shutter Speed 1/1000, F/22,  32k, Super-Resolution, award winning,", 
         "high detail, warm lighting, godrays, vivid, beautiful, trending on artstation, by jordan grimmer, huge scene, grass, art greg rutkowski ", 
         "highly detailed, digital painting, artstation, illustration, art by artgerm and greg rutkowski and alphonse mucha.", 
         "Charlie Bowater, stanley artgerm lau, a character portrait, sots art, sharp focus, smooth, aesthetic, extremely detailed, octane render,solo, dark industrial background, rtx, rock clothes, cinematic light, intricate detail, highly detailed, high res, detailed facial features", 
        "portrait photograph" , "realistic" , "concept art" , "elegant, highly detailed" , "intricate, sharp focus, depth of field, f/1. 8, 85mm, medium shot, mid shot, (((professionally color graded)))" ," sharp focus, bright soft diffused light" , "(volumetric fog),", 
        "Cinematic film still" ," (dark city street:1.2)" , "(cold colors), damp, moist, intricate details" ,"shallow depth of field, [volumetric fog]" , "cinematic lighting, reflections, photographed on a Canon EOS R5, 50mm lens, F/2.8, HDR, 8k resolution" , "cinematic film still from cyberpunk movie" , "volumetric fog,  (RAW, analog, masterpiece, best quality, soft particles, 8k, flawless perfect face, intricate details" , "trending on artstation, trending on cgsociety, dlsr, ultra sharp, hdr, rtx, antialiasing, canon 5d foto))" , "((skin details, high detailed skin texture))" , "(((perfect face))), (perfect eyes)))", 

        # Add other prompt options here...
    ]

    if prompt_text:
        chosen_option = random.choice(options)
        return text_gen(f"{prompt_text}, {chosen_option} - {timestamp}")
    else:
        return text_gen("", timestamp)

def initialize_api_connection():
    global headers
    API_TOKEN = os.getenv("HF_READ_TOKEN") # it is free
    headers = {"Authorization": f"Bearer {API_TOKEN}"}

# Run initialization functions on startup
initialize_api_connection()

@app.get("/generate_prompts")
def generate_prompts(prompt_text: str):
    return get_prompts(prompt_text)



from fastapi import Query

from fastapi import HTTPException

@app.get("/send_inputs")
def send_inputs(
    inputs: str,
    noise_level: float,
    is_negative: str,
    steps: int = 20,
    cfg_scale: int = 4.5,
    seed: int = None
):
    try:
        generated_image = generate_image(inputs, is_negative, steps, cfg_scale, seed)
        if generated_image is not None:
            image_bytes = io.BytesIO()
            generated_image.save(image_bytes, format="JPEG")
            image_base64 = base64.b64encode(image_bytes.getvalue()).decode("utf-8")
            return {"image_base64": image_base64}
        else:
            # Return an error message if the image couldn't be generated
            raise HTTPException(status_code=500, detail="Failed to generate image.")
    except Exception as e:
        # Log the error and return an error message
        print(f"Error generating image: {e}")
        raise HTTPException(status_code=500, detail="Failed to generate image.")


app.mount("/", StaticFiles(directory="static", html=True), name="static")

@app.get("/")
def index() -> FileResponse:
    return FileResponse(path="/app/static/index.html", media_type="text/html")