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One Path to Bankruptcy for Replit

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,     ...
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Gmail Data Mining : An Easy Way to Conquer those Nextdoor (KIND) and Facebook Posts :)

You're able to locate (say) a 100 emails in gmail with a search - say "nextdoor west atherton". Now, you want to (say) grab the name, community name and post-subject for the various posts that are part of this email. How? First , check the box on the menu-bar. Then, you'll see a link that you can click on to "Select all conversations that match this search." Click that.  Lastly, click on the three vertical dots and pick "Forward as attachment" Send it to yourself. Then, view that email, and, you'll see an icon with this tooltip : Use this to download to your PC. And then? How do you grep. You have a bunch of .eml files. Look at any of them and you quickly see that the "encoding" has made your life painful :). That's where automation comes in - namely python scripting :) You want to process all of these files and dump out the text of the body to see if it's amenable to parsing. This much you can do with this simple script : T...
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Align an Embedded Image in Jupyter Markdown

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 : ![bad_pie](attachment:Capture.PNG) Than...
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openCV : Really Filtering by Color

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, ...
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One Path to Bankruptcy for Replit

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,     ...
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