Upload app.py.py

app.py.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)