Merge pull request #6 from UPstartDeveloper/lighter-model

Lighter model

Author: UPstartDeveloper

.gitignore

@@ -11,7 +11,7 @@ __pycache__/
 .vscode
 
 # Keras Models
-*.h5
+model/*
 
 # Distribution / packaging
 .Python

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2020 Mansar Youness
+Copyright (c) 2021 Zain Raza
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

app/config.yaml

@@ -1,5 +1,5 @@
 resize_shape: [256, 256]
 targets: ['Fire_Images']
-model_path: "fire_classifier.h5"
-model_url: "https://github.com/UPstartDeveloper/Fire-Detection-API/releases/download/v0.0.1/fire_classifier.h5"
-model_sha256: "b527ecea0f4786e45eb0242f263752c45fb55754"
+model_paths: ["fire_classifier_params.h5", "fire_classifier_layers.json"]
+base_model_url: "https://github.com/UPstartDeveloper/Fire-Detection-API/releases/download/v0.0.2"
+model_sha256: "26f32ae0666bbb83e11968935db0ec2ab06623d1"

app/main.py

@@ -3,8 +3,8 @@
 from fire_classifier.predictor import ImagePredictor
 
 app = FastAPI(
-    title="Fire Detection API", 
-    description="Informs the probability that an image contains fire."
+    title="Fire Detection API",
+    description="Informs the probability that an image contains fire.",
 )
 
 predictor_config_path = "./app/config.yaml"
@@ -14,5 +14,5 @@
 
 @app.post("/classify-image/")
 def create_upload_file(file: UploadFile = File(...)):
-    '''Predicts the possibility that a RBG image contains fire.'''
+    """Predicts the possibility that a RBG image contains fire."""
     return predictor.predict_from_file(file.file)

fire_classifier/predictor.py

@@ -1,42 +1,48 @@
 import argparse
 
 import numpy as np
+from typing import List
 import yaml
-from tensorflow.keras.models import load_model
 
 from fire_classifier.preprocessing_utilities import (
     read_img_from_path,
     read_from_file,
 )
-from fire_classifier.utils import download_model
+from fire_classifier.utils import download_model, load_model
 
 
 class ImagePredictor:
-    def __init__(self, model_path, resize_size, targets):
-        self.model_path = model_path
-        self.model = load_model(self.model_path)
+    def __init__(
+        self, model_paths: List[str], resize_size: List[int], 
+        base_download_url: str, targets: List[str]
+    ):
+        self.model_paths = model_paths
         self.resize_size = resize_size
+        self.model = load_model(base_download_url, self.model_paths)
         self.targets = targets
 
     @classmethod
     def init_from_config_path(cls, config_path):
+        # load details for setting up the model
         with open(config_path, "r") as f:
             config = yaml.load(f, yaml.SafeLoader)
+        # use the config data, to integrate the model into the new object
         predictor = cls(
-            model_path=config["model_path"],
+            model_paths=config["model_paths"],
             resize_size=config["resize_shape"],
+            base_download_url=config["base_model_url"],
             targets=config["targets"],
         )
         return predictor
 
     @classmethod
     def init_from_config_url(cls, config_path):
-        with open(config_path, "r") as f:
-            config = yaml.load(f, yaml.SafeLoader)
+        # with open(config_path, "r") as f:
+        #     config = yaml.load(f, yaml.SafeLoader)
 
-        download_model(
-            config["model_url"], config["model_path"], config["model_sha256"]
-        )
+        # download_model(
+        #     config["model_file_urls"], config["model_paths"], config["model_sha256"]
+        # )
 
         return cls.init_from_config_path(config_path)
 

fire_classifier/utils.py

@@ -1,6 +1,6 @@
 import hashlib
 import os
-from tensorflow.keras.utils import get_file
+from tensorflow import keras
 
 
 def get_hash(filename):
@@ -12,8 +12,33 @@ def get_hash(filename):
     return sha256_hash.hexdigest()
 
 
-def download_model(url, file_path, file_sha256):
-    if os.path.exists(file_path) and get_hash(file_path) == file_sha256:
+def download_model(url, file_paths, file_sha256=None):
+    params_file, layers_file = file_paths
+    params_url, layers_url = (
+        f"{url}/{params_file}",
+        f"{url}/{layers_file}"
+    )
+    if (os.path.exists(params_file) and os.path.exists(layers_file)
+        # and get_hash(layers_file) == file_sha256
+    ):
         print("File already exists")
-    else:
-        get_file(origin=url, fname=file_path, cache_dir=".", cache_subdir="")
+    else:  # download the model
+        keras.utils.get_file(
+            origin=layers_url, fname=layers_file,
+            cache_dir='.', cache_subdir="./model"
+        )
+        keras.utils.get_file(
+            origin=params_url, fname=params_file,
+            cache_dir='.', cache_subdir="./model"
+        )
+
+def load_model(url, file_paths):
+    '''Model reconstruction using H5 + JSON'''
+    # First download the model, if needed
+    download_model(url, file_paths)
+    params_file, layers_file = file_paths
+    # Model reconstruction
+    with open(f"./model/{layers_file}") as f:
+        model = keras.models.model_from_json(f.read())
+        model.load_weights(f"./model/{params_file}")
+    return model

setup.py

@@ -11,22 +11,24 @@
 except:
     REQUIRED = []
 
-setup(name='YOUR_API_NAMME',
-      version='0.1.0',
-      description='ADD_A_DESCRIPTION',
-      author='YOUR_NAME',
-      author_email='YOUR_EMAIL',
-      url='YOUR_REPO_URL',
-      license='MIT',
-      install_requires=REQUIRED,
-      classifiers=[
-          'Intended Audience :: Developers',
-          'Intended Audience :: Education',
-          'Intended Audience :: Science/Research',
-          'License :: OSI Approved :: MIT License',
-          'Programming Language :: Python :: 3',
-          'Programming Language :: Python :: 3.6',
-          'Topic :: Software Development :: Libraries',
-          'Topic :: Software Development :: Libraries :: Python Modules'
-      ],
-      packages=find_packages(exclude=("example", "app", "data", "docker", "tests")))
+setup(
+    name="YOUR_API_NAMME",
+    version="0.1.0",
+    description="ADD_A_DESCRIPTION",
+    author="YOUR_NAME",
+    author_email="YOUR_EMAIL",
+    url="YOUR_REPO_URL",
+    license="MIT",
+    install_requires=REQUIRED,
+    classifiers=[
+        "Intended Audience :: Developers",
+        "Intended Audience :: Education",
+        "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.6",
+        "Topic :: Software Development :: Libraries",
+        "Topic :: Software Development :: Libraries :: Python Modules",
+    ],
+    packages=find_packages(exclude=("example", "app", "data", "docker", "tests")),
+)