Create app.py

app.py

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+import gradio as gr
+import zipfile
+from glob import glob
+
+from PIL import Image
+from collections import Counter
+
+from huggingface_hub import hf_hub_download, login 
+login(token=os.getenv('LOGIN_TOKEN'))
+hf_hub_download(repo_id="giniwini/model_creator", filename="ModelCreator.py", )
+
+from ModelCreator import Model
+
+m = Model()
+
+markdown_head = """# <center>🚀👁️ Create your own Classifier 👁️🚀</center>      
+### By Elok Quence 🦾
+
+This space is intended to give high level tools so everyone can make his own image classification model and use it for any purpose. 
+
+## How to create it and test it
+
+1. Put some images in a folder classified by subfolders indicating the classes. Like: \n
+				- img_folder/cat/image_of_cat_0.png
+				- img_folder/dog/image_of_dog_4.jpeg \n
+I recomment around 5 images per class. \n
+2. Right click in the folder and press "compress..." in ".zip" mode. This will create you a zip file. \n
+3. Upload the file on the space. \n
+4. Once uploaded, try with some images to test that everything is going well and have fun.  
+
+---
+## ⬇️⬇️⬇️ Do you want to export the model or thank my effort? 🫀 Look at the bottom to see the options. ⬇️⬇️⬇️
+---
+"""
+
+markdown_tail = """
+---
+
+**Are you grateful?** 
+Consider buying me a coffee subscribing to my [patreon](https://patreon.com/elokquentia?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink) coffee tier \n
+or make a donation trough [paypal](https://www.paypal.com/donate/?hosted_button_id=QD2W2G34GWQ4J)
+---
+
+## Export the model
+Subscribe to patreon Brunch tier to get the password. [patreon](https://patreon.com/elokquentia?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink)
+
+---
+## Have you already exported the model? Here's how to use it
+
+### 1. Install the requirements: 
+Open the terminal and go to the directory where you placed the requirements.txt file. \n 
+Now with this command you can install the dependencies at once. 
+```bash
+pip install -r requirements.txt
+```
+Has anything gone wrong? Look inside the requirements.txt and install the dependencies one by one. 
+
+### 2. Use ModelCreator's Model class
+Place the ModelCreator.py file on the directory you want to code. 
+    - some_folder/ModelCreator.py
+    - your_python_script.py
+
+### 3. Use your model
+In your_python_script.py, try something like:
+```python
+from ModelCreator import Model
+
+model_path = 'model.pickle'
+m2 = Model().load_model(model_path)
+
+image_path = 'some_image_path/image_1.png'
+result = m2(image_path)
+print(result)
+```
+### 4. DO YOU HAVE ANY DOUBTS ON HOW THIS WORKS OR PROBLEMS USING THE MODEL?
+Contact me on huggingface or via e-mail [email protected]
+"""
+
+
+def fit_model(zip_file_path):
+    path = 'tmp/'
+    with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+        zip_ref.extractall(path)
+
+    images_path = glob(f'{path}**/*/*.*')
+    labels_train = [i.split('/')[-2] for i in images_path]
+    print(labels_train)
+    images_train = [Image.open(i) for i in images_path]
+
+    m.train(images_train, labels_train)
+    return (f"Model Fitted \n"
+            f"Classes: {dict(Counter(labels_train))}")
+
+
+def predict(image):
+    l = m.predict(image)
+    return f'Predicted Class: {l}'
+
+
+def export(password):
+    if password == os.getenv('EXPORT_PASSWORD'):
+        m.export_model()
+        return [[gr.update(visible=True), file] for file in [f'model.pickle', f'requirements.txt', f'ModelCreator.py']]
+    else:
+        return [None, f'Subscribe to Patreon to Download']*3
+
+with gr.Blocks() as demo:
+    gr.Markdown(markdown_head)
+
+    with gr.Row() as g1:
+        inp = gr.File(label="zip file")
+        out = gr.Textbox()
+    btn = gr.Button("Submit Zip File")
+    btn.click(fn=fit_model, inputs=inp, outputs=out)
+
+    with gr.Row() as g2:
+        inp2 = gr.Image(label="Input Image", type='pil')
+        out2 = gr.Textbox()
+    btn2 = gr.Button("Predict/Test on an Image")
+    btn2.click(fn=predict, inputs=inp2, outputs=out2)
+
+    with gr.Row() as g3:
+        inp3 = gr.Textbox()
+        out3 = gr.File(label='Download link', visible=True, height=30, interactive=False)
+        out4 = gr.File(label='Download link', visible=True, height=30, interactive=False)
+        out5 = gr.File(label='Download link', visible=True, height=30, interactive=False)
+    btn3 = gr.Button("Export Fitted Model")
+    btn3.click(fn=export, inputs=inp3, outputs=[[out3, out3],[out4, out4],[out5, out5]])
+    gr.Markdown(markdown_tail)
+demo.launch()