changes

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+**/.vscode/
+**/env/
+**/runs/
+**/models/
+**/logs/
+**/.clang-format
+**/datasets/

app.py

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+import gradio as gr
+from model import create_effnetb2_model
+import os
+import torch
+from torch import nn
+from typing import List, Dict, Tuple
+from timeit import default_timer as timer
+
+with open('food101_classes.txt', 'r') as f:
+    class_names = f.read().splitlines()
+
+model, transforms = create_effnetb2_model(num_classes=len(class_names))
+
+ckpt = torch.load('effnetb2_stepdecay_50epochs.tar', map_location='cpu')
+model.load_state_dict(ckpt['model_state_dict'])
+model.to('cpu')
+
+
+def predict(img) -> Tuple[Dict, float]:
+    start = timer()
+    img = transforms(img)
+    img = img.unsqueeze(0)
+    img = img.to('cpu')
+    model.to('cpu')
+    model.eval()
+    with torch.inference_mode():
+        pred_logits = model(img)
+        pred_probs = nn.Softmax(dim=1)(pred_logits).squeeze(0)
+        pred_probs_dict = {class_names[i]: pred_probs[i].item() for i in range(len(class_names))}
+    end = timer()
+    return pred_probs_dict, round(end - start, 4)
+
+
+examples_dir = 'examples'
+examples = [[os.path.join(examples_dir, f)] for f in os.listdir(examples_dir)]
+
+import gradio as gr
+title = "Food101 Image Classifier 🥘"
+description = "This efficientnetb2 model finetuned on Food101 dataset for 50 epochs with step decay scheduler."
+article = "Udemy PyTorch Bootcamp: Created for practice using [Gradio](https://www.gradio.app/)"
+demo = gr.Interface(fn=predict,
+                    inputs=gr.Image(type="pil", label="Image"),
+                    outputs=[gr.Label(label="Predictions", num_top_classes=5),
+                             gr.Number(label="Prediction Time (s)")],
+                    examples=examples,
+                    title=title,
+                    description=description,
+                    article=article)
+demo.launch(share=True)

effnetb2_stepdecay_50epochs.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4f2b70f5f980e6b83aab9bd844d697fa6d3a2b49587017bc44640a37a96994a2
+size 32978470

food101_classes.txt

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+apple_pie
+baby_back_ribs
+baklava
+beef_carpaccio
+beef_tartare
+beet_salad
+beignets
+bibimbap
+bread_pudding
+breakfast_burrito
+bruschetta
+caesar_salad
+cannoli
+caprese_salad
+carrot_cake
+ceviche
+cheese_plate
+cheesecake
+chicken_curry
+chicken_quesadilla
+chicken_wings
+chocolate_cake
+chocolate_mousse
+churros
+clam_chowder
+club_sandwich
+crab_cakes
+creme_brulee
+croque_madame
+cup_cakes
+deviled_eggs
+donuts
+dumplings
+edamame
+eggs_benedict
+escargots
+falafel
+filet_mignon
+fish_and_chips
+foie_gras
+french_fries
+french_onion_soup
+french_toast
+fried_calamari
+fried_rice
+frozen_yogurt
+garlic_bread
+gnocchi
+greek_salad
+grilled_cheese_sandwich
+grilled_salmon
+guacamole
+gyoza
+hamburger
+hot_and_sour_soup
+hot_dog
+huevos_rancheros
+hummus
+ice_cream
+lasagna
+lobster_bisque
+lobster_roll_sandwich
+macaroni_and_cheese
+macarons
+miso_soup
+mussels
+nachos
+omelette
+onion_rings
+oysters
+pad_thai
+paella
+pancakes
+panna_cotta
+peking_duck
+pho
+pizza
+pork_chop
+poutine
+prime_rib
+pulled_pork_sandwich
+ramen
+ravioli
+red_velvet_cake
+risotto
+samosa
+sashimi
+scallops
+seaweed_salad
+shrimp_and_grits
+spaghetti_bolognese
+spaghetti_carbonara
+spring_rolls
+steak
+strawberry_shortcake
+sushi
+tacos
+takoyaki
+tiramisu
+tuna_tartare
+waffles

model.py

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+from torch import nn
+from torchvision.models import efficientnet_b2, EfficientNet_B2_Weights, vit_b_16, ViT_B_16_Weights
+
+
+def create_effnetb2_model(num_classes=3):
+    weights_effnetb2 = EfficientNet_B2_Weights.DEFAULT
+    transforms_effnetb2 = weights_effnetb2.transforms()
+    model_effnetb2 = efficientnet_b2(weights=weights_effnetb2)
+    for param in model_effnetb2.parameters():
+        param.requires_grad = False
+    model_effnetb2.classifier[1] = nn.Linear(in_features=1408, out_features=num_classes)
+    return model_effnetb2, transforms_effnetb2
+
+
+def create_vitb16_model(num_classes=3):
+    weights_vitb16 = ViT_B_16_Weights.DEFAULT
+    transforms_vitb16 = weights_vitb16.transforms()
+    model_vitb16 = vit_b_16(weights=weights_vitb16)
+    for param in model_vitb16.parameters():
+        param.requires_grad = False
+    model_vitb16.heads[0] = nn.Linear(in_features=768, out_features=num_classes)
+    return model_vitb16, transforms_vitb16

requirements.txt

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+torch>=2.2.0
+torchvision>=0.17.0
+gradio>=4.26.0