Deploy machine learning model | using streamlit | iris flower webapp

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Hey in this video I explained how to deploy your deep learning model using streamlit module. Specifically in this video I created a iris flower prediction script so I also explained the code for the model creation saving the model. And after that we created webapp using streamlit and loaded that model. Created basic form field for user input and button to calculate prediction.
After this video you will have understanding of following topics,
* Iris Flower Deep Learning model
* Model loading and saving
* Streamlit module
* Webapp creation
* Showing image using streamlit
* and few more basics...

timestamps,
0:00 - Introduction and Installation
0:35 - Basics of streamlit ( important )
4:00 - Iris model building and saving
11:20 - Model Deployment using streamlit
15:05 - Showing Image outputs
16:25 - Testing

If you have any queries regarding any of the topic I discussed in this video feel free to talk to e using below links:
instagram : @programming_hut

keys,
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Nice
I'm waiting for the one using flask

localpedia

For all 3-prediction of iris-species getting same image. How to solve this problem?

vibhagowda

Code doesn't work properly according the images you used ...

samratpaul

Nice Video, But Are you sure that when you are dumping labels.npy, that file's 1st entry belongs to firstCategorical_Index, 2nd entry belongs to SecondCategorical_Index, 3rd entry to ThirdCategorical_Index? Dicts are not ordered, so any key can be the first. [i.e. label[some_key3]=2 be the first in the label dict and label[some_key2]=1, label[some_key1]=0 are second and third in the dict label, then [some_key3, some_key2, some_key1] will be our labels.npy file. In app, Say for some test_X, the model predicts ArgMax as 2, and then it will incorrectly predict label as "some_key1" using the dump labels.npy]. Please check this and clarify.

alokranjansrivastava

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