Upload main (4).py

main (4).py

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+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
+
+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}"}
+def generate_image(prompt):
+    try:
+        payload = {"inputs": prompt}
+        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 Exception as e:
+        # Handle the error gracefully, such as logging the error or returning a default image
+        print(f"Error generating image: {e}")
+        return None  # Return None or a default image in case of error
+
+
+
+
+
+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 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)
+
+@app.get("/generate_prompts")
+def generate_prompts(prompt_text: str):
+    return get_prompts(prompt_text)
+
+
+
+@app.get("/send_inputs")
+def send_inputs(inputs: str, noise_level: float):
+    try:
+        generated_image = generate_image(inputs)
+        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
+            return {"error": "Failed to generate image."}
+    except Exception as e:
+        # Log the error and return an error message
+        print(f"Error generating image: {e}")
+        return {"error": "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")
+    
+