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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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What's Missing from JimShaped?

A slick tutorial showing you how to use tkInter to make a Minesweeper game.

But, if you don't have time to get through the whole thing in one sitting. Remember, if you're not a crack python coder, and I'm not, and you want to try doing the thing "on your own" while you watch, you're going to take longer than two hours. Can't rule out my brain slowing down being part of it too.

Now, what should he do? What needs to be different?

Follow a software engineering approach.

Create a list of requirements that you refer to often. Create the entity-relationships diagram - or describe what you're trying to build in the MVC lingo. It can't hurt.

This will help someone who comes back for a bit every weekend trying to finish this up.

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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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