Update app.py

app.py

@@ -1,152 +1,256 @@
+#!/usr/bin/env python
+#patch 2.0 ()
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# ...
 import os
-import shutil
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import plotly.express as px
+import random
+import uuid
+import json
 import gradio as gr
+import numpy as np
+from PIL import Image
+import spaces
+import torch
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+
+#Load the HTML content
+#html_file_url = "https://prithivmlmods-hamster-static.static.hf.space/index.html"
+#html_content = f'<iframe src="{html_file_url}" style="width:100%; height:180px; border:none;"></iframe>'
+#html_file_url = "https://prithivmlmods-static-loading-theme.static.hf.space/index.html"
+
+#html_file_url = "https://prithivhamster.vercel.app/"
+#html_content = f'<iframe src="{html_file_url}" style="width:100%; height:400px; border:none"></iframe>'
+
+DESCRIPTIONx = """## STABLE HAMSTER 🐹
+
+"""
 
 css = '''
-.gradio-container{max-width: 900px !important}
+.gradio-container{max-width: 560px !important}
 h1{text-align:center}
+footer {
+    visibility: hidden
+}
 '''
 
-def create_visualizations(data):
-    plots = []
-    
-    # Create figures directory
-    figures_dir = "./figures"
-    shutil.rmtree(figures_dir, ignore_errors=True)
-    os.makedirs(figures_dir, exist_ok=True)
-    
-    # Histograms for numeric columns
-    numeric_cols = data.select_dtypes(include=['number']).columns
-    for col in numeric_cols:
-        plt.figure()
-        sns.histplot(data[col], kde=True)
-        plt.title(f'Histogram of {col}')
-        plt.xlabel(col)
-        plt.ylabel('Frequency')
-        hist_path = os.path.join(figures_dir, f'histogram_{col}.png')
-        plt.savefig(hist_path)
-        plt.close()
-        plots.append(hist_path)
-    
-    # Box plots for numeric columns
-    for col in numeric_cols:
-        plt.figure()
-        sns.boxplot(x=data[col])
-        plt.title(f'Box Plot of {col}')
-        box_path = os.path.join(figures_dir, f'boxplot_{col}.png')
-        plt.savefig(box_path)
-        plt.close()
-        plots.append(box_path)
-    
-    # Scatter plot matrix
-    if len(numeric_cols) > 1:
-        plt.figure()
-        sns.pairplot(data[numeric_cols])
-        plt.title('Scatter Plot Matrix')
-        scatter_matrix_path = os.path.join(figures_dir, 'scatter_matrix.png')
-        plt.savefig(scatter_matrix_path)
-        plt.close()
-        plots.append(scatter_matrix_path)
-    
-    # Correlation heatmap
-    if len(numeric_cols) > 1:
-        plt.figure()
-        corr = data[numeric_cols].corr()
-        sns.heatmap(corr, annot=True, cmap='coolwarm')
-        plt.title('Correlation Heatmap')
-        heatmap_path = os.path.join(figures_dir, 'correlation_heatmap.png')
-        plt.savefig(heatmap_path)
-        plt.close()
-        plots.append(heatmap_path)
-    
-    # Bar charts for categorical columns
-    categorical_cols = data.select_dtypes(include=['object']).columns
-    if not categorical_cols.empty:
-        for col in categorical_cols:
-            plt.figure()
-            data[col].value_counts().plot(kind='bar')
-            plt.title(f'Bar Chart of {col}')
-            plt.xlabel(col)
-            plt.ylabel('Count')
-            bar_path = os.path.join(figures_dir, f'bar_chart_{col}.png')
-            plt.savefig(bar_path)
-            plt.close()
-            plots.append(bar_path)
+examples = [
+    "3d image, cute girl, in the style of Pixar --ar 1:2 --stylize 750, 4K resolution highlights, Sharp focus, octane render, ray tracing, Ultra-High-Definition, 8k, UHD, HDR, (Masterpiece:1.5), (best quality:1.5)",
+    "Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K",
+    "Vector illustration of a horse, vector graphic design with flat colors on an brown background in the style of vector art, using simple shapes and graphics with simple details, professionally designed as a tshirt logo ready for print on a white background. --ar 89:82 --v 6.0 --style raw",
+    "Man in brown leather jacket posing for camera, in the style of sleek and stylized, clockpunk, subtle shades, exacting precision, ferrania p30  --ar 67:101 --v 5",
+    "Commercial photography, giant burger, white lighting, studio light, 8k octane rendering, high resolution photography, insanely detailed, fine details, on white isolated plain, 8k, commercial photography, stock photo, professional color grading, --v 4 --ar 9:16 "
     
-    # Line charts (if a 'date' column is present)
-    if 'date' in data.columns:
-        plt.figure()
-        data['date'] = pd.to_datetime(data['date'])
-        data.set_index('date').plot()
-        plt.title('Line Chart of Date Series')
-        line_chart_path = os.path.join(figures_dir, 'line_chart.png')
-        plt.savefig(line_chart_path)
-        plt.close()
-        plots.append(line_chart_path)
-    
-    # Scatter plot using Plotly
-    if len(numeric_cols) >= 2:
-        fig = px.scatter(data, x=numeric_cols[0], y=numeric_cols[1], title='Scatter Plot')
-        scatter_plot_path = os.path.join(figures_dir, 'scatter_plot.html')
-        fig.write_html(scatter_plot_path)
-        plots.append(scatter_plot_path)
-    
-    # Pie chart for categorical columns (only the first categorical column)
-    if not categorical_cols.empty:
-        fig = px.pie(data, names=categorical_cols[0], title='Pie Chart of ' + categorical_cols[0])
-        pie_chart_path = os.path.join(figures_dir, 'pie_chart.html')
-        fig.write_html(pie_chart_path)
-        plots.append(pie_chart_path)
-    
-    # Heatmaps (e.g., for a correlation matrix or cross-tabulation)
-    if len(numeric_cols) > 1:
-        heatmap_data = data[numeric_cols].corr()
-        fig = px.imshow(heatmap_data, text_auto=True, title='Heatmap of Numeric Variables')
-        heatmap_plot_path = os.path.join(figures_dir, 'heatmap_plot.html')
-        fig.write_html(heatmap_plot_path)
-        plots.append(heatmap_plot_path)
-    
-    # Violin plots for numeric columns
-    for col in numeric_cols:
-        plt.figure()
-        sns.violinplot(x=data[col])
-        plt.title(f'Violin Plot of {col}')
-        violin_path = os.path.join(figures_dir, f'violin_plot_{col}.png')
-        plt.savefig(violin_path)
-        plt.close()
-        plots.append(violin_path)
-    
-    return plots
+]
 
-def analyze_data(file_input):
-    data = pd.read_csv(file_input.name)
-    return create_visualizations(data)
 
-# Example file path
-example_file_path = "./example/example.csv"
+#examples = [
+  #  ["file/1.png", "3d image, cute girl, in the style of Pixar --ar 1:2 --stylize 750, 4K resolution highlights, Sharp focus, octane render, ray tracing, Ultra-High-Definition, 8k, UHD, HDR, (Masterpiece:1.5), (best quality:1.5)"],
+   # ["file/2.png", "Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K"],
+    #["file/3.png", "Vector illustration of a horse, vector graphic design with flat colors on a brown background in the style of vector art, using simple shapes and graphics with simple details, professionally designed as a tshirt logo ready for print on a white background. --ar 89:82 --v 6.0 --style raw"],
+    #["file/4.png", "Man in brown leather jacket posing for the camera, in the style of sleek and stylized, clockpunk, subtle shades, exacting precision, ferrania p30  --ar 67:101 --v 5"],
+    #["file/5.png", "Commercial photography, giant burger, white lighting, studio light, 8k octane rendering, high resolution photography, insanely detailed, fine details, on a white isolated plain, 8k, commercial photography, stock photo, professional color grading, --v 4 --ar 9:16"]
+#]
 
-with gr.Blocks(css=css, theme=gr.themes.Soft(primary_hue=gr.themes.colors.orange, secondary_hue=gr.themes.colors.blue)) as demo:
-    gr.Markdown("# DATA BOARD📊\nUpload a `.csv` file to generate various visualizations and interactive plots.")
-    
-    file_input = gr.File(label="Upload your `.csv` file")
-    submit = gr.Button("Generate Dashboards")
-    
-    # Display images and interactive plots in a gallery
-    gallery = gr.Gallery(label="Visualizations")
-    
-    # Example block with cache_examples set to True
-    examples = gr.Examples(
-        examples=[[example_file_path]],
-        inputs=file_input,
-        outputs=gallery,
-        cache_examples=True  # Enable caching
+
+#Set an os.Getenv variable
+#set VAR_NAME=”VALUE”
+#Fetch an environment variable
+#echo %VAR_NAME%
+
+MODEL_ID = os.getenv("MODEL_VAL_PATH") #Use SDXL Model as "MODEL_REPO" --------->>> ”VALUE”. 
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))  # Allow generating multiple images at once
+
+#Load model outside of function
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    use_safetensors=True,
+    add_watermarker=False,
+).to(device)
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+
+# <compile speedup >
+if USE_TORCH_COMPILE:
+    pipe.compile()
+
+# Offloading capacity (RAM)
+if ENABLE_CPU_OFFLOAD:
+    pipe.enable_model_cpu_offload()
+
+MAX_SEED = np.iinfo(np.int32).max
+
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+@spaces.GPU(duration=60, enable_queue=True)
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 1,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    num_inference_steps: int = 25,
+    randomize_seed: bool = False,
+    use_resolution_binning: bool = True, 
+    num_images: int = 1,  # Number of images to generate
+    progress=gr.Progress(track_tqdm=True),
+):
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    #Options 
+    options = {
+        "prompt": [prompt] * num_images,
+        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
+        "width": width,
+        "height": height,
+        "guidance_scale": guidance_scale,
+        "num_inference_steps": num_inference_steps,
+        "generator": generator,
+        "output_type": "pil",
+    }
+
+    #VRAM usage Lesser
+    if use_resolution_binning:
+        options["use_resolution_binning"] = True
+
+    #Images potential batches
+    images = []
+    for i in range(0, num_images, BATCH_SIZE):
+        batch_options = options.copy()
+        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
+        if "negative_prompt" in batch_options:
+            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
+        images.extend(pipe(**batch_options).images)
+
+    image_paths = [save_image(img) for img in images]
+    return image_paths, seed
+#Main gr.Block
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+    gr.Markdown(DESCRIPTIONx)  
+
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            run_button = gr.Button("Run", scale=0)
+        result = gr.Gallery(label="Result", columns=1, show_label=False) 
+    with gr.Accordion("Advanced options", open=False, visible=False):
+        num_images = gr.Slider(
+            label="Number of Images",
+            minimum=1,
+            maximum=4,
+            step=1,
+            value=1,
+        )
+        with gr.Row():
+            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=5,
+                lines=4,
+                placeholder="Enter a negative prompt",
+                value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
+                visible=True,
+            )
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Row(visible=True):
+            width = gr.Slider(
+                label="Width",
+                minimum=512,
+                maximum=MAX_IMAGE_SIZE,
+                step=64,
+                value=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=512,
+                maximum=MAX_IMAGE_SIZE,
+                step=64,
+                value=1024,
+            )
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance Scale",
+                minimum=0.1,
+                maximum=6,
+                step=0.1,
+                value=3.0,
+            )
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps",
+                minimum=1,
+                maximum=25,
+                step=1,
+                value=23,
+            )
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        cache_examples=False
     )
-    
-    submit.click(analyze_data, file_input, gallery)
+
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
+
+    gr.on(
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            randomize_seed,
+            num_images
+        ],
+        outputs=[result, seed],
+        api_name="run",
+    )   
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.queue(max_size=40).launch()