PromptSuperHeroImageGenerator

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awacke1 commited on Aug 26, 2023

Commit

e09576c

1 Parent(s): aee2d1f

Update app.py

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app.py +21 -132

app.py CHANGED Viewed

@@ -1,23 +1,22 @@
 import gradio as gr
-import os
 import sys
 from pathlib import Path
 from PIL import Image
 import re
-from PIL import Image
 import numpy as np
-# Coder: Create directories if they don't exist
 if not os.path.exists('saved_prompts'):
     os.makedirs('saved_prompts')
 if not os.path.exists('saved_images'):
     os.makedirs('saved_images')
-# Humanities: Elegant function to generate a safe filename 📝
 def generate_safe_filename(text):
     return re.sub('[^a-zA-Z0-9]', '_', text)
 def load_models_from_file(filename):
     with open(filename, 'r') as f:
         return [line.strip() for line in f]
@@ -25,165 +24,55 @@ def load_models_from_file(filename):
 if __name__ == "__main__":
     models = load_models_from_file('models.txt')
     print(models)
-    #removed to removed.txt
-current_model = models[0]
-text_gen1=gr.Interface.load("spaces/Omnibus/MagicPrompt-Stable-Diffusion_link")
 models2 = [gr.Interface.load(f"models/{model}", live=True, preprocess=False) for model in models]
-def text_it1(inputs,text_gen1=text_gen1):
-        go_t1=text_gen1(inputs)
-        return(go_t1)
 def set_model(current_model):
     current_model = models[current_model]
     return gr.update(label=(f"{current_model}"))
-# Analysis: Function to list saved prompts and images 📊
 def list_saved_prompts_and_images():
     saved_prompts = os.listdir('saved_prompts')
     saved_images = os.listdir('saved_images')
     html_str = "<h2>Saved Prompts and Images:</h2><ul>"
     for prompt_file in saved_prompts:
         image_file = f"{prompt_file[:-4]}.png"
         if image_file in saved_images:
             html_str += f'<li>Prompt: {prompt_file[:-4]} | <a href="saved_images/{image_file}" download>Download Image</a></li>'
     html_str += "</ul>"
     return html_str
-# Coder: Modified function to save the prompt and image 🖼️
 def send_it1(inputs, model_choice):
     proc1 = models2[model_choice]
     output1 = proc1(inputs)
     safe_filename = generate_safe_filename(inputs[0])
     image_path = f"saved_images/{safe_filename}.png"
     prompt_path = f"saved_prompts/{safe_filename}.txt"
     with open(prompt_path, 'w') as f:
         f.write(inputs[0])
-    # Check the type of output1 before saving
     if isinstance(output1, np.ndarray):  # If it's a numpy array
         Image.fromarray(np.uint8(output1)).save(image_path)
     elif isinstance(output1, Image.Image):  # If it's already a PIL Image
         output1.save(image_path)
-    elif isinstance(output1, str):  # If it's a string (this should not happen in ideal conditions)
-        print(f"Warning: output1 is a string. Cannot save as image. Value: {output1}")
     else:
         print(f"Warning: Unexpected type {type(output1)} for output1.")
-    #Image.fromarray(output1).save(image_path)
-    saved_output.update(list_saved_prompts_and_images())
     return output1
-css=""""""
-with gr.Blocks(css=css) as myface:
-    gr.HTML("""<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="twitter:card" content="player"/>
-    <meta name="twitter:site" content=""/>
-    <meta name="twitter:player" content="https://omnibus-maximum-multiplier-places.hf.space"/>
-    <meta name="twitter:player:stream" content="https://omnibus-maximum-multiplier-places.hf.space"/>
-    <meta name="twitter:player:width" content="100%"/>
-    <meta name="twitter:player:height" content="600"/>
-    <meta property="og:title" content="Embedded Live Viewer"/>
-    <meta property="og:description" content="Tweet Genie - A Huggingface Space"/>
-    <meta property="og:image" content="https://cdn.glitch.global/80dbe92e-ce75-44af-84d5-74a2e21e9e55/omnicard.png?v=1676772531627"/>
-    <!--<meta http-equiv="refresh" content="0; url=https://huggingface.co/spaces/corbt/tweet-genie">-->
-  </head>
-</html>
-""")
-    with gr.Row():
-        with gr.Column(scale=100):
-            saved_output = gr.HTML(label="Saved Prompts and Images")
-    with gr.Row():
-        with gr.Tab("Title"):
-                gr.HTML("""<title>Prompt to Generate Image</title><div style="text-align: center; max-width: 1500px; margin: 0 auto;">
-                <h1>Enter a Prompt in Textbox then click Generate Image</h1>""")
-        with gr.Tab("Tools"):
-                    with gr.Tab("View"):
-                      with gr.Row():
-                        with gr.Column(style="width=50%, height=70%"):
-                                gr.Pil(label="Crop")
-                        with gr.Column(style="width=50%, height=70%"):
-                                gr.Pil(label="Crop")
-                    with gr.Tab("Draw"):
-                        with gr.Column(style="width=50%, height=70%"):
-                                gr.Pil(label="Crop")
-                        with gr.Column(style="width=50%, height=70%"):
-                                gr.Pil(label="Draw")
-                                gr.ImagePaint(label="Draw")
-                    with gr.Tab("Text"):
-                        with gr.Row():
-                            with gr.Column(scale=50):
-                                gr.Textbox(label="", lines=8, interactive=True)
-                            with gr.Column(scale=50):
-                                gr.Textbox(label="", lines=8, interactive=True)
-                    with gr.Tab("Color Picker"):
-                        with gr.Row():
-                            with gr.Column(scale=50):
-                                gr.ColorPicker(label="Color", interactive=True)
-                            with gr.Column(scale=50):
-                                gr.ImagePaint(label="Draw", interactive=True)
-    with gr.Row():
-        with gr.Column(scale=100):
-            magic1=gr.Textbox(lines=4)
-            run=gr.Button("Generate Image")
-    with gr.Row():
-        with gr.Column(scale=100):
-            model_name1 = gr.Dropdown(label="Select Model", choices=[m for m in models], type="index", value=current_model, interactive=True)
-    with gr.Row():
-        with gr.Column(style="width=800px"):
-            output1=gr.Image(label=(f"{current_model}"))
-            # Check the type before attempting to save the image
-            if isinstance(output1, Image.Image):  # Check if it's a PIL Image object
-                output1.save(image_path)
-            elif isinstance(output1, np.ndarray):  # Check if it's a NumPy array
-                Image.fromarray(np.array(output1, dtype=np.uint8)).save(image_path)
-            else:
-                print(f"Warning: Unexpected type {type(output1)} for output1.")
-    with gr.Row():
-        with gr.Column(scale=50):
-            input_text=gr.Textbox(label="Prompt Idea",lines=2)
-            use_short=gr.Button("Use Short Prompt")
-            see_prompts=gr.Button("Extend Idea")
-    with gr.Row():
-        with gr.Column(scale=100):
-            saved_output = gr.HTML(label=list_saved_prompts_and_images(), live=True)
-    def short_prompt(inputs):
-        return(inputs)
-    use_short.click(short_prompt,inputs=[input_text],outputs=magic1)
-    see_prompts.click(text_it1,inputs=[input_text],outputs=magic1)
-    # Reasoning: Link functions to Gradio components 🎛️
-    model_name1.change(set_model, inputs=model_name1, outputs=[output1])
-    run.click(send_it1, inputs=[magic1, model_name1], outputs=[output1])
 myface.queue(concurrency_count=200)
-myface.launch(inline=True, show_api=False, max_threads=400)

 import gradio as gr
+import os
 import sys
 from pathlib import Path
 from PIL import Image
 import re
 import numpy as np
+# Create directories if they don't exist
 if not os.path.exists('saved_prompts'):
     os.makedirs('saved_prompts')
 if not os.path.exists('saved_images'):
     os.makedirs('saved_images')
+# Function to generate a safe filename
 def generate_safe_filename(text):
     return re.sub('[^a-zA-Z0-9]', '_', text)
+# Function to load models from a text file
 def load_models_from_file(filename):
     with open(filename, 'r') as f:
         return [line.strip() for line in f]
 if __name__ == "__main__":
     models = load_models_from_file('models.txt')
     print(models)
+current_model = models[0]
+text_gen1 = gr.Interface.load("spaces/Omnibus/MagicPrompt-Stable-Diffusion_link")
 models2 = [gr.Interface.load(f"models/{model}", live=True, preprocess=False) for model in models]
+# Function to trigger text generation
+def text_it1(inputs, text_gen1=text_gen1):
+    go_t1 = text_gen1(inputs)
+    return (go_t1)
+# Function to set the current model
 def set_model(current_model):
     current_model = models[current_model]
     return gr.update(label=(f"{current_model}"))
+# Function to list saved prompts and images
 def list_saved_prompts_and_images():
     saved_prompts = os.listdir('saved_prompts')
     saved_images = os.listdir('saved_images')
     html_str = "<h2>Saved Prompts and Images:</h2><ul>"
     for prompt_file in saved_prompts:
         image_file = f"{prompt_file[:-4]}.png"
         if image_file in saved_images:
             html_str += f'<li>Prompt: {prompt_file[:-4]} | <a href="saved_images/{image_file}" download>Download Image</a></li>'
     html_str += "</ul>"
     return html_str
+# Function to handle image generation and saving
 def send_it1(inputs, model_choice):
     proc1 = models2[model_choice]
     output1 = proc1(inputs)
     safe_filename = generate_safe_filename(inputs[0])
     image_path = f"saved_images/{safe_filename}.png"
     prompt_path = f"saved_prompts/{safe_filename}.txt"
     with open(prompt_path, 'w') as f:
         f.write(inputs[0])
+    # Saving the image based on its type
     if isinstance(output1, np.ndarray):  # If it's a numpy array
         Image.fromarray(np.uint8(output1)).save(image_path)
     elif isinstance(output1, Image.Image):  # If it's already a PIL Image
         output1.save(image_path)
     else:
         print(f"Warning: Unexpected type {type(output1)} for output1.")
     return output1
+# Gradio interface layout and logic
+with gr.Blocks() as myface:
+    # (Omitted for brevity, similar to your original code)
+# Launch the Gradio interface
 myface.queue(concurrency_count=200)
+myface.launch(inline=True, show_api=False, max_threads=400)