Delete app.py

app.py

@@ -1,109 +0,0 @@
-import os
-import random
-import numpy as np
-import torch
-import gradio as gr
-from diffusers import StableDiffusionPipeline
-import paramiko
-from huggingface_hub import login
-
-# Hugging Face Token
-HF_TOKEN = os.getenv('HF_TOKEN', '').strip()
-if not HF_TOKEN:
-    raise ValueError("HUGGING_TOKEN is not set. Please set the token as an environment variable.")
-
-# Hugging Face Login
-login(token=HF_TOKEN)
-
-# Konfiguration
-STORAGE_DOMAIN = os.getenv('STORAGE_DOMAIN', '').strip() # SFTP Server Domain
-STORAGE_USER = os.getenv('STORAGE_USER', '').strip() # SFTP User
-STORAGE_PSWD = os.getenv('STORAGE_PSWD', '').strip() # SFTP Passwort
-STORAGE_PORT = int(os.getenv('STORAGE_PORT', '22').strip()) # SFTP Port
-STORAGE_SECRET = os.getenv('STORAGE_SECRET', '').strip() # Secret Token
-
-# Modell-Optionen - können angepasst werden
-MODEL_REPO = os.getenv('MODEL_REPO', 'stabilityai/stable-diffusion-3-medium-diffusers')  # Standard-Modell
-TORCH_DTYPE = os.getenv('TORCH_DTYPE', 'float16')  # Standard-Präzision
-
-# Modell laden
-device = "cuda" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if TORCH_DTYPE == 'float16' else torch.float32
-
-try:
-    pipe = StableDiffusionPipeline.from_pretrained(
-        MODEL_REPO, 
-        torch_dtype=torch_dtype
-    ).to(device)
-except Exception as e:
-    raise RuntimeError(f"Failed to load the model. Ensure the token has access to the repo. Error: {e}")
-
-# Maximalwerte
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1344
-
-# SFTP-Funktion
-def upload_to_sftp(local_file, remote_path):
-    try:
-        transport = paramiko.Transport((STORAGE_DOMAIN, STORAGE_PORT))
-        transport.connect(username=STORAGE_USER, password=STORAGE_PSWD)
-        sftp = paramiko.SFTPClient.from_transport(transport)
-        sftp.put(local_file, remote_path)
-        sftp.close()
-        transport.close()
-        print(f"File {local_file} successfully uploaded to {remote_path}")
-        return True
-    except Exception as e:
-        print(f"Error during SFTP upload: {e}")
-        return False
-
-# Inferenz-Funktion
-def infer(prompt, width, height, guidance_scale, num_inference_steps, seed, randomize_seed):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    
-    generator = torch.manual_seed(seed)
-    image = pipe(
-        prompt, 
-        guidance_scale=guidance_scale, 
-        num_inference_steps=num_inference_steps, 
-        width=width, 
-        height=height, 
-        generator=generator
-    ).images[0]
-    
-    # Speichere Bild lokal
-    local_file = f"/tmp/generated_image_{seed}.png"
-    image.save(local_file)
-    
-    # Hochladen zu SFTP
-    remote_path = f"/uploads/generated_image_{seed}.png"
-    if upload_to_sftp(local_file, remote_path):
-        os.remove(local_file)
-        return f"Image uploaded to {remote_path}", seed
-    else:
-        return "Failed to upload image", seed
-
-# App-Titel mit Modell- und Präzisionsinformationen
-APP_TITLE = f"### Stable Diffusion - {os.path.basename(MODEL_REPO)} ({TORCH_DTYPE} auf {device})"
-
-# Gradio-App
-with gr.Blocks() as demo:
-    gr.Markdown(APP_TITLE)
-    prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
-    width = gr.Slider(256, MAX_IMAGE_SIZE, step=64, value=512, label="Width")
-    height = gr.Slider(256, MAX_IMAGE_SIZE, step=64, value=512, label="Height")
-    guidance_scale = gr.Slider(0.0, 10.0, step=0.1, value=7.5, label="Guidance Scale")
-    num_inference_steps = gr.Slider(1, 50, step=1, value=25, label="Inference Steps")
-    seed = gr.Number(value=42, label="Seed")
-    randomize_seed = gr.Checkbox(value=False, label="Randomize Seed")
-    generate_button = gr.Button("Generate Image")
-    output = gr.Text(label="Output")
-    
-    generate_button.click(
-        infer,
-        inputs=[prompt, width, height, guidance_scale, num_inference_steps, seed, randomize_seed],
-        outputs=[output, seed]
-    )
-
-demo.launch()