Python Kaizen - The Infinite Loop of Improvement

Nice thing is that you don't have to depend on the image existing as a separate file that you can refer to. You can embed it like an image in an email - you get the idea. Jupyter takes care of this for you in the .ipynb file. But, by default, the image is aligned center and is default size. What if you want to set the size? If it were an external file, then you can just resort to standard HTML. But, you want a fully self contained notebook. So? In one cell, above this one, NOT markdown, but code, have an HTML magic where you specify CSS that applies to this TAG. In the cell of interest, where you insert the image after doing Edit > Insert Image, change the "alt text" inside the [] to something the CSS style can refer to and you're done So, (1) looks like : %%html <style>     img[alt=bad_pie]{         float : left;     } </style> And, the cell with the image, when in edit mode, will look like :  Than...

The free openCV crash course : img_NZ_bgr = cv.imread('New_Zealand_Lake.jpg', cv.IMREAD_COLOR) b,g,r = cv.split(img_NZ_bgr) plt.figure(figsize=[20,5]) plt.subplot(141);plt.imshow(r, cmap='gray');plt.title("Red") plt.subplot(142);plt.imshow(b, cmap='gray');plt.title("Blue") plt.subplot(143);plt.imshow(g, cmap='gray');plt.title("Green") # merging imgMerged = cv.merge((b,g,r)) # original code : b,g,r plt.subplot(144);plt.imshow(imgMerged[...,::-1]);plt.title("Merged") Gives you : Coolie McVoolie. But, wait a minute! Are you really going to fall for that? Remember those "3D" glasses you got in magazines as a kid that let you see the page in 3D by using filters (each eye sees the picture from the required angle)? Meaning, if you're looking at the Red channel, you want to see : This! Right? How? Easy Make a blank channel (basically using NumPy zeros) Use that blank channel for the filtered channels, ...

User trying to import a module that's not installed. Instead of bumping him and telling her to use a different workspace on which the module *is* installed, use compute resources to try and install.. nuts.. Starting with : from transformers import TFAutoModelForSeq2SeqLM, AutoTokenizer import gradio as gr model = TFAutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small") tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small") def gen_text(input_string, max_length):     inputs = tokenizer(input_string, return_tensors="pt")     outputs = model.generate(**inputs, max_length=max_length)     final_text = tokenizer.batch_decode(outputs[0], skip_special_tokens=True)     return (final_text) demo = gr.Interface(                                                          fn=gen_text,     ...