Delete app.py

app.py

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-# -*- coding: utf-8 -*-
-"""Untitled3.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1BltKPv_n-glCuuIIYSBA6GHK-tmwbl20
-"""
-
-import torch
-from PIL import Image
-from torchvision import transforms
-import gradio as gr
-import json
-import urllib, urllib.request
-from diffusers import DiffusionPipeline
-from transformers import pipeline
-
-# First Page
-def demo_tab(image):
-    # The demo tab simply returns the same image as input
-    return image
-
-# Secibd Page
-def generate_image(Prompt,Negative_prompt,Steps):
-  # load both base & refiner
-  base = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-  )
-  base.to("cuda")
-  refiner = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-1.0",
-    text_encoder_2=base.text_encoder_2,
-    vae=base.vae,
-    torch_dtype=torch.float16,
-    use_safetensors=True,
-    variant="fp16",
-  )
-  refiner.to("cuda")
-
-# Define how many steps and what % of steps to be run on each experts (80/20) here
-  high_noise_frac = 0.8
-  prompt = Prompt
-  negative_prompt = Negative_prompt
-  n_steps = Steps
-  # run both experts
-  image = base(
-      prompt=prompt,
-      negative_prompt=negative_prompt,
-      num_inference_steps=n_steps,
-      denoising_end=high_noise_frac,
-      output_type="latent",
-  ).images
-  image = refiner(
-      prompt=prompt,
-      num_inference_steps=n_steps,
-      denoising_start=high_noise_frac,
-      image=image,
-  ).images[0]
-  return image
-
-def predict(input_image):
-  model = torch.hub.load('RF5/danbooru-pretrained', 'resnet50')
-  model.eval()
-
-# Load JSON file from github as Label
-  with urllib.request.urlopen("https://github.com/RF5/danbooru-pretrained/raw/master/config/class_names_6000.json") as url:
-      labels = json.loads(url.read().decode())
-      #Convert input image from array to PIL Image
-      input_image = Image.fromarray(input_image.astype('uint8'), 'RGB')
-      #Preprocess the input image
-      preprocess = transforms.Compose([
-          transforms.Resize(360),
-          transforms.ToTensor(),
-          transforms.Normalize(mean=[0.7137, 0.6628, 0.6519], std=[0.2970, 0.3017, 0.2979]),
-      ])
-      input_tensor = preprocess(input_image)
-      input_batch = input_tensor.unsqueeze(0)
-
-      # Use CUDA if available
-      if torch.cuda.is_available():
-          input_batch = input_batch.to('cuda')
-          model.to('cuda')
-
-      # Make prediction
-      with torch.no_grad():
-          output = model(input_batch)
-
-      # Get probabilities
-      probs = torch.sigmoid(output[0])
-
-      # Convert tensor to Python list of floats
-      probs = probs.cpu().numpy().tolist()
-
-      # Sort labels with probabilities and return top 10
-      sorted_labels_with_probs = sorted(list(zip(labels, probs)), key=lambda x: x[1], reverse=True)[:10]
-
-      # Convert list of tuples to dictionary and convert numpy floats to Python floats
-      sorted_labels_with_probs_dict = {label: float(prob) for label, prob in sorted_labels_with_probs}
-      return sorted_labels_with_probs_dict
-
-def image_classify(input_image, model):
-    model_mapping= {
-      "Resnet 50": "microsoft/resnet-50",
-      "Vit Base Patch16-224": "google/vit-base-patch16-224",
-      "NSFW Image Detection": "Falconsai/nsfw_image_detection",
-      "Vit Age Classifier": "nateraw/vit-age-classifier"
-    }
-    classifier = pipeline("image-classification", model=model_mapping[model])
-    img = input_image
-    result = classifier(img)
-    #Sort the perccentage confident from highest to lowest
-    highest_confidence_result = sorted(result, key=lambda x: x['score'], reverse=True)[0]
-    # Format the score as a percentage and combine it with the label
-    output = f"{highest_confidence_result['score']*100:.2f}% confident : {highest_confidence_result['label']}"
-    return output
-
-# Define the demo tab
-with gr.Blocks() as demo:
-  with gr.Tab("Demo"):
-    image_input = gr.Image(type='pil')
-    image_output = gr.Image()
-    demo_button = gr.Button("Generate")
-
-  with gr.Tab("Text2Image"):
-    SD_text_input = gr.Textbox(lines=5, label="Prompt")
-    SD_text2_input = gr.Textbox(lines=5, label="Negative Prompt")
-    Slider_input = gr.Slider(0, 100, label="Strength")
-    SD_output = gr.Image()
-    SD_button = gr.Button("Generate")
-
-  with gr.Tab("Text2Image"):
-    option_input = gr.Dropdown(["resnet50", "vit-base-patch16-224", "vit-age-classifier", "nsfw image classification"], label="Model")
-    t2i_input = gr.Image(label="Image")
-    t2i_output = gr.Image()
-    t2i_button = gr.Button("Generate")
-
-    demo_button.click(demo_tab, inputs=image_input, outputs=image_output)
-    SD_button.click(generate_image, inputs=[SD_text_input,SD_text2_input,Slider_input], outputs=SD_output)
-    t2i_button.click(image_classify, inputs=[option_input,t2i_input], outputs=t2i_button)
-
-demo.launch(debug=True)