Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+from pathlib import Path
+from transformers import CLIPTokenizer
+import torch
+import subprocess
+import os
+import random
+
+from nanograd.models.stable_diffusion import model_loader, pipeline
+
+# Configure devices
+DEVICE = "cpu"
+ALLOW_CUDA = False 
+ALLOW_MPS = True
+
+if torch.cuda.is_available() and ALLOW_CUDA:
+    DEVICE = "cuda"
+elif torch.backends.mps.is_available() and ALLOW_MPS:
+    DEVICE = "mps"
+print(f"Using device: {DEVICE}")
+
+# Load Stable Diffusion model
+tokenizer_vocab_path = Path("C:\\Users\\Esmail\\Desktop\\nanograd\\nanograd\\models\\stable_diffusion\\sd_data\\tokenizer_vocab.json")
+tokenizer_merges_path = Path("C:\\Users\\Esmail\\Desktop\\nanograd\\nanograd\\models\\stable_diffusion\\sd_data\\tokenizer_merges.txt")
+model_file = Path("C:\\Users\\Esmail\\Desktop\\nanograd\\nanograd\\models\\stable_diffusion\\sd_data\\v1-5-pruned-emaonly.ckpt")
+
+tokenizer = CLIPTokenizer(str(tokenizer_vocab_path), merges_file=str(tokenizer_merges_path))
+models = model_loader.preload_models_from_standard_weights(str(model_file), DEVICE)
+
+# Blueprints for image generation and text generation
+blueprints = {
+    "Visual Story": {
+        "sd_prompts": [
+            "A futuristic city skyline at dusk, flying cars, neon lights, cyberpunk style",
+            "A bustling marketplace in a futuristic city, holograms, diverse crowd",
+            "A serene park in a futuristic city with advanced technology blending with nature"
+        ],
+        "sd_cfg_scales": [9, 8, 7],
+        "sd_num_inference_steps": [60, 50, 45],
+        "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"],
+        "ollama_prompts": [
+            "Describe a futuristic city that blends natural elements with advanced technology.",
+            "Write about an advanced cityscape with unique technological elements.",
+            "Imagine a futuristic metropolis where nature and technology harmoniously coexist."
+        ],
+        "ollama_models": ["llama3", "aya", "codellama"]
+    },
+    # Other blueprints with similar structure...
+    "Nature & Poetry": {
+        "sd_prompts": [
+            "A peaceful mountain landscape at sunrise, photorealistic, serene",
+            "A tranquil lake surrounded by autumn trees, soft light, misty atmosphere",
+            "A hidden waterfall in a dense jungle, lush greenery, crystal clear water"
+        ],
+        "sd_cfg_scales": [9, 8, 7],
+        "sd_num_inference_steps": [60, 50, 45],
+        "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"],
+        "ollama_prompts": [
+            "Write a short poem about a tranquil sunrise over the mountains.",
+            "Describe the beauty of a hidden waterfall in a jungle.",
+            "Compose a poetic reflection on the serenity of a lake at dawn."
+        ],
+        "ollama_models": ["llama3", "aya", "codellama"]
+    },
+    # Additional blueprints with multiple prompts...
+    "Dreamscape": {
+        "sd_prompts": [
+            "A surreal dreamscape with floating islands and bioluminescent creatures",
+            "An endless horizon of strange landscapes, blending day and night",
+            "A fantastical world with floating rocks and neon-lit skies"
+        ],
+        "sd_cfg_scales": [9, 8, 7],
+        "sd_num_inference_steps": [60, 50, 45],
+        "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"],
+        "ollama_prompts": [
+            "Describe a dreamlike world filled with wonder and mystery.",
+            "Write about a place where time doesn't exist, only dreams.",
+            "Create a story where reality and fantasy blur together."
+        ],
+        "ollama_models": ["llama3", "aya", "codellama"]
+    },
+    "Abstract Art": {
+        "sd_prompts": [
+            "Abstract painting with vibrant colors and dynamic shapes",
+            "A digital artwork with chaotic patterns and bold contrasts",
+            "Geometric abstraction with a focus on form and color"
+        ],
+        "sd_cfg_scales": [9, 8, 7],
+        "sd_num_inference_steps": [60, 50, 45],
+        "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"],
+        "ollama_prompts": [
+            "Write a short description of an abstract painting.",
+            "Describe a piece of modern art that defies traditional norms.",
+            "Imagine a world where art is created by emotions, not hands."
+        ],
+        "ollama_models": ["llama3", "aya", "codellama"]
+    },
+    "Fashion Design": {
+        "sd_prompts": [
+            "A high-fashion model wearing a futuristic outfit, neon colors, catwalk pose",
+            "A chic ensemble blending classic elegance with modern flair",
+            "Avant-garde fashion with bold textures and unconventional shapes"
+        ],
+        "sd_cfg_scales": [9, 8, 7],
+        "sd_num_inference_steps": [60, 50, 45],
+        "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"],
+        "ollama_prompts": [
+            "Describe a unique and innovative fashion design.",
+            "Write about a new fashion trend inspired by nature.",
+            "Imagine a clothing line that combines style with sustainability."
+        ],
+        "ollama_models": ["llama3", "aya", "codellama"]
+    },
+    "Food & Recipe": {
+        "sd_prompts": [
+            "Abstract painting with vibrant colors and dynamic shapes",
+            "A digital artwork with chaotic patterns and bold contrasts",
+            "Geometric abstraction with a focus on form and color"
+        ],
+        "sd_cfg_scales": [9, 8, 7],
+        "sd_num_inference_steps": [60, 50, 45],
+        "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"],
+        "ollama_prompts": [
+            "Write a short description of an abstract painting.",
+            "Describe a piece of modern art that defies traditional norms.",
+            "Imagine a world where art is created by emotions, not hands."
+        ],
+        "ollama_models": ["llama3", "aya", "codellama"]
+    },
+    "Interior Design": {
+        "sd_prompts": [
+            "A modern living room with sleek furniture, minimalist design, and natural light",
+            "A cozy study room with rich textures, warm colors, and elegant decor",
+            "An open-plan kitchen with contemporary appliances and stylish finishes"
+        ],
+        "sd_cfg_scales": [9, 8, 7],
+        "sd_num_inference_steps": [60, 50, 45],
+        "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"],
+        "ollama_prompts": [
+            "Describe an interior design that combines modern and classic elements.",
+            "Write about a space that enhances productivity and relaxation through design.",
+            "Imagine a luxurious interior design for a high-end apartment."
+        ],
+        "ollama_models": ["llama3", "aya", "codellama"]
+    },
+    "Historical Fiction": {
+        "sd_prompts": [
+            "A bustling Victorian-era street with horse-drawn carriages and period architecture",
+            "A grand historical ballroom with opulent decor and elegantly dressed guests",
+            "An ancient battlefield with detailed historical accuracy and dramatic scenery"
+        ],
+        "sd_cfg_scales": [9, 8, 7],
+        "sd_num_inference_steps": [60, 50, 45],
+        "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"],
+        "ollama_prompts": [
+            "Describe a significant historical event as if it were a scene in a novel.",
+            "Write about a character navigating the challenges of a historical setting.",
+            "Imagine a historical figure interacting with modern technology."
+        ],
+        "ollama_models": ["llama3", "aya", "codellama"]
+    },
+    "Science Fiction": {
+        "sd_prompts": [
+            "A futuristic cityscape with flying cars, neon lights, and towering skyscrapers",
+            "An alien planet with unique landscapes, strange flora, and advanced technology",
+            "A space station with cutting-edge design and high-tech equipment"
+        ],
+        "sd_cfg_scales": [9, 8, 7],
+        "sd_num_inference_steps": [60, 50, 45],
+        "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"],
+        "ollama_prompts": [
+            "Describe a futuristic world where technology has reshaped society.",
+            "Write about an encounter with an alien civilization.",
+            "Imagine a story set in a distant future with advanced technology and space exploration."
+        ],
+        "ollama_models": ["llama3", "aya", "codellama"]
+    },
+    "Character Design": {
+        "sd_prompts": [
+            "A detailed fantasy character with elaborate costumes and accessories",
+            "A sci-fi hero with futuristic armor and high-tech gadgets",
+            "A historical figure portrayed with accurate attire and realistic features"
+        ],
+        "sd_cfg_scales": [9, 8, 7],
+        "sd_num_inference_steps": [60, 50, 45],
+        "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"],
+        "ollama_prompts": [
+            "Describe a unique character from a fantasy novel, focusing on their appearance and personality.",
+            "Write about a futuristic character with advanced technology and a compelling backstory.",
+            "Imagine a historical figure as a character in a modern setting."
+        ],
+        "ollama_models": ["llama3", "aya", "codellama"]
+    }
+}
+
+# Define functions for each feature
+def generate_image(prompt, cfg_scale, num_inference_steps, sampler):
+    uncond_prompt = ""
+    do_cfg = True
+    input_image = None
+    strength = 0.9
+    seed = 42
+
+    output_image = pipeline.generate(
+        prompt=prompt,
+        uncond_prompt=uncond_prompt,
+        input_image=input_image,
+        strength=strength,
+        do_cfg=do_cfg,
+        cfg_scale=cfg_scale,
+        sampler_name=sampler,
+        n_inference_steps=num_inference_steps,
+        seed=seed,
+        models=models,
+        device=DEVICE,
+        idle_device="cpu",
+        tokenizer=tokenizer,
+    )
+
+    output_image = Image.fromarray(output_image)
+    return output_image
+
+def apply_blueprint(blueprint_name):
+    if blueprint_name in blueprints:
+        bp = blueprints[blueprint_name]
+        sd_prompts = random.choice(bp["sd_prompts"])
+        sd_cfg_scale = random.choice(bp["sd_cfg_scales"])
+        sd_num_inference_steps = random.choice(bp["sd_num_inference_steps"])
+        sd_sampler = random.choice(bp["sd_samplers"])
+        ollama_prompts = random.choice(bp["ollama_prompts"])
+        ollama_model = random.choice(bp["ollama_models"])
+        return (
+            sd_prompts, sd_cfg_scale, sd_num_inference_steps, sd_sampler, 
+            ollama_model, ollama_prompts
+        )
+    return "", 7, 20, "ddpm", "aya", ""
+
+def download_checkpoint(checkpoint):
+    try:
+        # Run the litgpt download command
+        command = ["litgpt", "download", checkpoint]
+        process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+        output, error = process.communicate()
+        if process.returncode == 0:
+            return f"Checkpoint '{checkpoint}' downloaded successfully.\n{output}"
+        else:
+            return f"Error downloading checkpoint '{checkpoint}':\n{error}"
+    except Exception as e:
+        return f"Unexpected error: {str(e)}"
+
+def chat_with_ollama(model_name, prompt):
+    command = ['ollama', 'run', model_name, prompt]
+    result = subprocess.run(command, capture_output=True, text=True)
+    return result.stdout
+
+def install_ollama():
+    try:
+        # Command to install Ollama silently
+        installer_path = "OllamaSetup.exe"
+        if not os.path.exists(installer_path):
+            # Download the installer if not already available
+            subprocess.run(["curl", "-o", installer_path, "https://ollama.com/download/OllamaSetup.exe"], check=True)
+
+        # Run the installer silently
+        subprocess.run([installer_path, "/S"], check=True)
+        return "Ollama installed successfully."
+    except Exception as e:
+        return f"Installation failed: {str(e)}"
+
+def welcome(name):
+    return f"Welcome to nanograd Engine, {name}!"
+
+js = """
+function createGradioAnimation() {
+    var container = document.createElement('div');
+    container.id = 'gradio-animation';
+    container.style.fontSize = '2em';
+    container.style.fontWeight = 'bold';
+    container.style.textAlign = 'center';
+    container.style.marginBottom = '20px';
+
+    var text = 'Welcome to nanograd Engine!';
+    for (var i = 0; i < text.length; i++) {
+        (function(i){
+            setTimeout(function(){
+                var letter = document.createElement('span');
+                letter.style.opacity = '0';
+                letter.style.transition = 'opacity 0.5s';
+                letter.innerText = text[i];
+
+                container.appendChild(letter);
+
+                setTimeout(function() {
+                    letter.style.opacity = '1';
+                }, 50);
+            }, i * 250);
+        })(i);
+    }
+
+    var gradioContainer = document.querySelector('.gradio-container');
+    gradioContainer.insertBefore(container, gradioContainer.firstChild);
+
+    return 'Animation created';
+}
+"""
+
+# Gradio interface
+def gradio_interface():
+    with gr.Blocks('ParityError/Interstellar', js=js) as demo:
+        with gr.Tab("nano-Engine"):
+            with gr.Row():
+                with gr.Column(scale=1): 
+                    # Text Generation with Ollama
+                    gr.Markdown("### Generate Text with Ollama")
+                    ollama_model_name = gr.Dropdown(label="Select Ollama Model", choices=["aya", "llama3", "codellama"], value="aya")
+                    ollama_prompts = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
+                    ollama_output = gr.Textbox(label="Output", placeholder="Model output will appear here", interactive=True)
+                    ollama_btn = gr.Button("Generate", variant="primary")
+                    ollama_btn.click(fn=chat_with_ollama, inputs=[ollama_model_name, ollama_prompts], outputs=ollama_output)
+
+                    gr.Markdown("### GPT Checkpoints Management")
+                    checkpoint_dropdown = gr.Dropdown(label="Select Checkpoint", choices=["EleutherAI/gpt-neo-125M", "EleutherAI/gpt-neo-1.3B", "microsoft/phi-2", "codellama/CodeLlama-13b-hf"], value="EleutherAI/gpt-neo-125M")
+                    download_btn = gr.Button("Download Checkpoint", variant="primary")
+                    checkpoint_status = gr.Textbox(label="Download Status", placeholder="Status will appear here", interactive=True)
+                    download_btn.click(fn=download_checkpoint, inputs=checkpoint_dropdown, outputs=checkpoint_status)
+
+                    gr.Markdown("### Install Ollama")
+                    install_ollama_btn = gr.Button("Install Ollama", variant="primary")
+                    installation_status = gr.Textbox(label="Installation Status", placeholder="Status will appear here", interactive=True)
+                    install_ollama_btn.click(fn=install_ollama, outputs=installation_status)
+
+                with gr.Column(scale=1):
+                    gr.Markdown("### Stable Diffusion Image Generation")
+                    
+                    prompt_input = gr.Textbox(label="Prompt", placeholder="A cat stretching on the floor, highly detailed, ultra sharp, cinematic, 100mm lens, 8k resolution")
+                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
+                    num_inference_steps = gr.Slider(label="Sampling Steps", minimum=10, maximum=100, value=20, step=5)
+                    sampler = gr.Radio(label="Sampling Method", choices=["ddpm", "Euler a", "Euler", "LMS", "Heun", "DPM2 a", "PLMS"], value="ddpm")
+                    generate_img_btn = gr.Button("Generate", variant="primary")
+                    output_image = gr.Image(label="Output", show_label=False, height=700, width=750)
+
+                    generate_img_btn.click(fn=generate_image, inputs=[prompt_input, cfg_scale, num_inference_steps, sampler], outputs=output_image)
+
+            with gr.Tab("Blueprints"):
+                with gr.Row():
+                    blueprint_dropdown = gr.Dropdown(label="Select Blueprint", choices=list(blueprints.keys()), value=list(blueprints.keys())[0])
+                    load_blueprint_btn = gr.Button("Load Blueprint", variant="primary")
+                    
+                    # Blueprint Outputs
+                    sd_prompt_output = gr.Textbox(label="SD Prompt", interactive=True)
+                    sd_cfg_output = gr.Slider(label="SD CFG Scale", minimum=1, maximum=20, step=1, interactive=True)
+                    sd_steps_output = gr.Slider(label="SD Sampling Steps", minimum=10, maximum=100, step=5, interactive=True)
+                    sd_sampler_output = gr.Radio(label="SD Sampler", choices=["ddpm", "Euler a", "Euler", "LMS", "Heun", "DPM2 a", "PLMS"], value="ddpm", interactive=True)
+                    ollama_model_output = gr.Dropdown(label="Ollama Model", choices=["aya", "llama3", "codellama"], value="aya", interactive=True)
+                    ollama_prompt_output = gr.Textbox(label="Ollama Prompt", interactive=True)
+
+                    def load_blueprint(blueprint_name):
+                        if blueprint_name in blueprints:
+                            bp = blueprints[blueprint_name]
+                            sd_prompts = random.choice(bp["sd_prompts"])
+                            sd_cfg_scale = random.choice(bp["sd_cfg_scales"])
+                            sd_num_inference_steps = random.choice(bp["sd_num_inference_steps"])
+                            sd_sampler = random.choice(bp["sd_samplers"])
+                            ollama_prompts = random.choice(bp["ollama_prompts"])
+                            ollama_model = random.choice(bp["ollama_models"])
+                            return (
+                                sd_prompts, sd_cfg_scale, sd_num_inference_steps, sd_sampler, 
+                                ollama_model, ollama_prompts
+                            )
+                        return "", 7, 20, "ddpm", "aya", ""
+
+                    def apply_loaded_blueprint(prompt, cfg_scale, num_inference_steps, sampler, model, ollama_prompts):
+                        return (
+                            gr.update(value=prompt), 
+                            gr.update(value=cfg_scale), 
+                            gr.update(value=num_inference_steps), 
+                            gr.update(value=sampler), 
+                            gr.update(value=model), 
+                            gr.update(value=ollama_prompts)
+                        )
+
+                    load_blueprint_btn.click(fn=load_blueprint, inputs=blueprint_dropdown, outputs=[sd_prompt_output, sd_cfg_output, sd_steps_output, sd_sampler_output, ollama_model_output, ollama_prompt_output])
+                    load_blueprint_btn.click(fn=apply_loaded_blueprint, inputs=[sd_prompt_output, sd_cfg_output, sd_steps_output, sd_sampler_output, ollama_model_output, ollama_prompt_output], outputs=[prompt_input, cfg_scale, num_inference_steps, sampler, ollama_model_name, ollama_prompts])
+
+        with gr.Tab("Chatbot-Prompts"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    from nanograd.models.GPT.tokenizer import tokenize
+                    gr.Markdown("<h1><center>BPE Tokenizer</h1></center>")
+                    iface = gr.Interface(fn=tokenize, inputs="text", outputs="json")
+                
+                with gr.Column(scale=1):
+                    from examples import ollama_prompted
+                    gr.Markdown("<h1><center>Chatbot (لغة عربية)</h1></center>")
+                    i = gr.Interface(
+                        fn=ollama_prompted.run,
+                        inputs=gr.Textbox(lines=1, placeholder="Ask a question about travel or airlines"),
+                        outputs=gr.Textbox(label="Aya's response"),
+                    )
+    
+    demo.launch()
+
+# Run the Gradio interface
+gradio_interface()