Delete app copy.py

app copy.py

@@ -1,149 +0,0 @@
-import gradio as gr
-import torch
-from diffusers import AutoPipelineForText2Image
-from transformers import BlipProcessor, BlipForConditionalGeneration
-from pathlib import Path
-import stone
-import requests
-import io
-import os
-from PIL import Image
-import spaces
-
-import matplotlib.pyplot as plt
-import numpy as np
-from matplotlib.colors import hex2color
-
-
-pipeline_text2image = AutoPipelineForText2Image.from_pretrained(
-    "stabilityai/sdxl-turbo",
-    torch_dtype=torch.float16,
-    variant="fp16",
-)
-pipeline_text2image = pipeline_text2image.to("cuda")
-
-
-@spaces.GPU
-def getimgen(prompt):
-
-    return pipeline_text2image(
-        prompt=prompt,
-        guidance_scale=0.0,
-        num_inference_steps=2
-    ).images[0]
-
-
-blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
-blip_model = BlipForConditionalGeneration.from_pretrained(
-    "Salesforce/blip-image-captioning-large",
-    torch_dtype=torch.float16
-).to("cuda")
-
-
-@spaces.GPU
-def blip_caption_image(image, prefix):
-    inputs = blip_processor(image, prefix, return_tensors="pt").to("cuda", torch.float16)
-    out = blip_model.generate(**inputs)
-    return blip_processor.decode(out[0], skip_special_tokens=True)
-
-def genderfromcaption(caption):
-    cc = caption.split()
-    if "man" in cc or "boy" in cc:
-        return "Man"
-    elif "woman" in cc or "girl" in cc:
-        return "Woman"
-    return "Unsure"
-
-def genderplot(genlist):    
-    order = ["Man", "Woman", "Unsure"]
-
-    # Sort the list based on the order of keys
-    words = sorted(genlist, key=lambda x: order.index(x))
-
-    # Define colors for each category
-    colors = {"Man": "lightgreen", "Woman": "darkgreen", "Unsure": "lightgrey"}
-
-    # Map each word to its corresponding color
-    word_colors = [colors[word] for word in words]
-
-    # Plot the colors in a grid with reduced spacing
-    fig, axes = plt.subplots(2, 5, figsize=(5,5))
-
-    # Adjust spacing between subplots
-    plt.subplots_adjust(hspace=0.1, wspace=0.1)
-
-    for i, ax in enumerate(axes.flat):
-        ax.set_axis_off()
-        ax.add_patch(plt.Rectangle((0, 0), 1, 1, color=word_colors[i]))
-
-    return fig
-
-def skintoneplot(hex_codes):
-    # Convert hex codes to RGB values
-    rgb_values = [hex2color(hex_code) for hex_code in hex_codes]
-
-    # Calculate luminance for each color
-    luminance_values = [0.299 * r + 0.587 * g + 0.114 * b for r, g, b in rgb_values]
-
-    # Sort hex codes based on luminance in descending order (dark to light)
-    sorted_hex_codes = [code for _, code in sorted(zip(luminance_values, hex_codes), reverse=True)]
-
-    # Plot the colors in a grid with reduced spacing
-    fig, axes = plt.subplots(2, 5, figsize=(5,5))
-
-    # Adjust spacing between subplots
-    plt.subplots_adjust(hspace=0.1, wspace=0.1)
-
-    for i, ax in enumerate(axes.flat):
-        ax.set_axis_off()
-        ax.add_patch(plt.Rectangle((0, 0), 1, 1, color=sorted_hex_codes[i]))
-
-    return fig
-
-@spaces.GPU
-def generate_images_plots(prompt):
-    foldername = "temp"
-    # Generate 10 images
-    images = [getimgen(prompt) for _ in range(10)]
-
-    Path(foldername).mkdir(parents=True, exist_ok=True)
-
-    genders = []
-    skintones = []
-
-    for image, i in zip(images, range(10)):
-        prompt_prefix = "photo of a "
-        caption = blip_caption_image(image, prefix=prompt_prefix)
-        image.save(f"{foldername}/image_{i}.png")
-        try:
-            skintoneres = stone.process(f"{foldername}/image_{i}.png", return_report_image=False)
-            tone = skintoneres['faces'][0]['dominant_colors'][0]['color']
-            skintones.append(tone)
-        except:
-            skintones.append(None)
-
-        genders.append(genderfromcaption(caption))
-
-    print(genders, skintones)
-
-    return images, skintoneplot(skintones), genderplot(genders)
-
-
-with gr.Blocks(title = "Skin Tone and Gender bias in SDXL Demo - Inference API") as demo:
-
-    gr.Markdown("# Skin Tone and Gender bias in SDXL Demo")
-
-    prompt = gr.Textbox(label="Enter the Prompt")
-    gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery", 
-                         columns=[5], rows=[2], object_fit="contain", height="auto")
-    btn = gr.Button("Generate images", scale=0)
-    with gr.Row(equal_height=True):
-        skinplot = gr.Plot(label="Skin Tone")
-        genplot = gr.Plot(label="Gender")
-
-
-    btn.click(generate_images_plots, inputs = prompt, outputs = [gallery, skinplot, genplot])
-
-
-
-demo.launch(debug=True)