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engine.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+import uvicorn
+
+from nano_engine import generate_image, apply_blueprint
+
+app = FastAPI()
+
+# Mount the 'static' directory to serve JavaScript, CSS, and HTML files
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+class ImageRequest(BaseModel):
+    blueprint_name: str
+
+@app.post("/generate-image/")
+async def generate_image_endpoint(request: ImageRequest):
+    # Call the function from engine.py to generate an image
+    sd_prompts, sd_cfg_scale, sd_num_inference_steps, sd_sampler, ollama_model, ollama_prompt = apply_blueprint(request.blueprint_name)
+    print(f"Sampler value: {sd_sampler}")  # Add this line for debugging
+    image = generate_image(sd_prompts, sd_cfg_scale, sd_num_inference_steps, sd_sampler)
+    
+    # Save the image and return its path
+    image_path = "static/generated_image.png"
+    image.save(image_path)
+    
+    return {"image_url": f"/static/generated_image.png"}
+
+
+@app.get("/static")
+async def get_homepage():
+    return FileResponse("static/index.html")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)

main.py

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+import nanograd
+
+from nanograd.RL import Cartpole, car # import reinforcement learning package
+# Cartpole.run()
+# car.run()
+
+###############################################################
+from nanograd.models.stable_diffusion import sd_inference
+sd_inference.run()
+
+##############################################################
+from nanograd.analysis_lab import sentiment_analysis
+# sentiment_analysis.run()
+
+############################################################
+from nanograd import generate_dataset 
+
+# generate_dataset.tokenize()
+
+###########################################################
+
+from nanograd.models.llama import inference_llama 
+from nanograd.models.GPT import inference_gpt
+from nanograd.models.GPT import tokenizer
+
+# inference_gpt.use_model()
+
+# inference_llama.use_model()
+
+# tokenizer.run_tokenizer()
+###########################################################
+from nanograd.models import ollama
+from nanograd.models import chat
+# ollama.run() # test the model. 
+# chat.chat_with_models()
+# chat.chat_models()
+###################################################
+
+
+# if __name__ == "__main__":
+#     from nanograd.nn.engine import Value
+
+#     a = Value(-4.0)
+#     b = Value(2.0)
+#     c = a + b
+#     d = a + b + b**3
+#     c += c + 1
+#     c += 1 + c + (-a)
+#     d += d * 2 + (b + a).relu()
+#     d += 3 * d + (b - a).relu()
+#     d += 3 * d + (b - a).sigmoid(5)
+#     e = c - d
+#     f = e**2
+#     g = f / 2.0
+#     g += 10.0 / f
+#     print(f'{g.data:.4f}') 
+#     g.backward()
+#     print(f'{a.grad:.4f}') 
+#     print(f'{b.grad:.4f}')  
+#     print(f'{e.grad:.4f}')  
+
+
+# import nanograd.nn.train_nn

requirements.txt

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+torch>=1.9.0
+torchvision>=0.10.0
+torchaudio>=0.9.0
+numpy
+pandas
+matplotlib
+transformers
+litgpt 

setup.py

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+from setuptools import setup, find_packages
+
+setup(
+    name='nanograd',
+    version='0.1.0',
+    packages=find_packages(),
+    install_requires=[
+        'torch',
+        'argparse',
+        'tensorboard',
+        'wget',
+        'transformers',
+        'litgpt',
+        'tiktoken',
+        'sentencepiece',
+        'tqdm',
+        'regex',
+        'gradio',
+        'trl',
+        'datasets',
+        'gym',
+        'tinygrad',
+    ],
+    entry_points={
+        'console_scripts': [
+            'nanograd=nanograd.nanograd_CLI:main',
+        ],
+    },
+)

theme.js

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+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';
+}