API improvements #4

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s481871 merged 3 commits from flask-ML into dev 2024-01-18 23:34:16 +01:00
12 changed files with 279 additions and 70 deletions
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from io import BytesIO from io import BytesIO
import torch import numpy as np
import torch.nn.functional as F
from PIL import Image from PIL import Image
from torchvision.models.resnet import resnet50, ResNet50_Weights from keras.src.applications.resnet import preprocess_input, decode_predictions
from torchvision.transforms import transforms from keras.applications.resnet import ResNet50
model = resnet50(weights=ResNet50_Weights.DEFAULT)
model.eval()
# Define the image transformations
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
def is_cat(image): """
Recognition file.
Model is ResNet50. Pretrained model to image recognition.
If model recognize cat then returns response with first ten CAT predictions.
If first prediction is not a cat then returns False.
If prediction is not a cat (is not within list_of_labels) then skips this prediction.
Format of response:
{
'label': {label}
'score': {score}
}
"""
model = ResNet50(weights='imagenet')
# PRIVATE Preprocess image method
def _preprocess_image(image):
try: try:
img = Image.open(BytesIO(image.read())) img = Image.open(BytesIO(image.read()))
img = img.resize((224, 224))
# Preprocess the image img_array = np.array(img)
img_t = preprocess(img) img_array = np.expand_dims(img_array, axis=0)
batch_t = torch.unsqueeze(img_t, 0) img_array = preprocess_input(img_array)
return img_array
# Make the prediction
out = model(batch_t)
# Apply softmax to get probabilities
probabilities = F.softmax(out, dim=1)
# Get the maximum predicted class and its probability
max_prob, max_class = torch.max(probabilities, dim=1)
max_prob = max_prob.item()
max_class = max_class.item()
# Check if the maximum predicted class is within the range 281-285
if 281 <= max_class <= 285:
return max_class, max_prob
else:
return max_class, None
except Exception as e: except Exception as e:
print("Error while processing the image:", e) print(f"Error preprocessing image: {e}")
return None return None
# Generate response
def _generate_response(decoded_predictions, list_of_labels):
results = {}
for i, (imagenet_id, label, score) in enumerate(decoded_predictions):
if i == 0 and label not in list_of_labels:
return None
if score < 0.01:
break
if label in list_of_labels:
results[len(results) + 1] = {"label": label, "score": round(float(score), 2)}
return results
# Cat detection
def detect_cat(image_file, list_of_labels):
img_array = _preprocess_image(image_file)
prediction = model.predict(img_array)
decoded_predictions = decode_predictions(prediction, top=10)[0]
return _generate_response(decoded_predictions, list_of_labels)

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# Api
Port -> 5000
endpoint -> /detect-cat
Key -> 'Image'
Value -> {UPLOADED_FILE}

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docs/docs.md Normal file
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# Api
Port -> 5000
endpoint -> api/v1/detect-cat
Key -> 'Image'
Value -> {UPLOADED_FILE}
Flask Rest API application to cat recognition.
If request is valid then send response with results of recognition.
If key named 'Image' in body does not occur then returns 400 (BAD REQUEST).
Otherwise, returns 200 with results of recognition.
Format of response:
```json
{
"lang": "{users_lang}",
"results": {
"{filename}": {
"isCat": "{is_cat}",
"results": {
"1": "{result}",
"2": "{result}",
"3": "{result}",
"4": "{result}",
"5": "{result}",
"6": "{result}",
"7": "{result}",
"8": "{result}",
"9": "{result}",
"10": "{result}"
}
}
},
"errors": [
"{error_message}",
"{error_message}"
]
}
```
Format of result:
```json
{
"label": "{label}",
"score": "{score}"
}
```
Example response:
```json
```

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language_label_mapper.py Normal file
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from jproperties import Properties
"""
Translator method.
If everything fine then returns translated labels.
Else throws an Exception and returns untranslated labels.
"""
def translate(to_translate, lang):
try:
config = Properties()
# Load properties file for given lang
with open(f"resources/{lang}.properties", 'rb') as config_file:
config.load(config_file, encoding='UTF-8')
# Translate labels for given to_translate dictionary
for index, label_info in to_translate.items():
label = label_info.get("label")
to_translate[index]["label"] = config.get(label).data
return to_translate, None
except Exception as e:
error_message = f"Error translating labels: {e}"
print(error_message)
return to_translate, error_message

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main.py
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from flask import Flask, request, jsonify, session from flask import Flask, request, Response, json
from cat_detection import detect_cat
from language_label_mapper import translate
from validator import validate
"""
Flask Rest API application to cat recognition.
If request is valid then send response with results of recognition.
If key named 'Image' in body does not occurred then returns 400 (BAD REQUEST).
Otherwise returns 200 with results of recognition.
Format of response:
{
"lang": {users_lang},
"results": {
{filename}: {
"isCat": {is_cat},
"results": {
"1": {result}
"2": {result}
"3": {result}
...
"10" {result}
}
},
...
},
errors[
{error_message},
{error_message},
...
]
}
To see result format -> cat_detection.py
"""
from cat_detection import is_cat
# Define flask app # Define flask app
app = Flask(__name__) app = Flask(__name__)
app.secret_key = 'secret_key' app.secret_key = 'secret_key'
# Available cats
list_of_labels = [
'lynx',
'lion',
'tiger',
'cheetah',
'leopard',
'jaguar',
'tabby',
'Egyptian_cat',
'cougar',
'Persian_cat',
'Siamese_cat',
'snow_leopard',
'tiger_cat'
]
@app.route('/detect-cat', methods=['POST']) # Available languages
languages = {'pl', 'en'}
@app.route('/api/v1/detect-cat', methods=['POST'])
def upload_file(): def upload_file():
# 'Key' in body should be named as 'image'. Type should be 'File' and in 'Value' we should upload image from disc. # Validate request
file = request.files['image'] error_messages = validate(request)
if file.filename == '':
return jsonify({'error': "File name is empty. Please name a file."}), 400
max_class, max_prob = is_cat(file)
# Save result in session # If any errors occurred, return 400 (BAD REQUEST)
session['result'] = max_class, max_prob if len(error_messages) > 0:
errors = json.dumps(
{
'errors': error_messages
}
)
return Response(errors, status=400, mimetype='application/json')
# Tworzenie komunikatu na podstawie wyniku analizy zdjęcia # Get files from request
translator = { files = request.files.getlist('image')
281: "tabby cat",
282: "tiger cat", # Get user's language (Value in header 'Accept-Language'). Default value is English
283: "persian cat", lang = request.accept_languages.best_match(languages, default='en')
284: "siamese cat",
285: "egyptian cat" # Define JSON structure for results
results = {
'lang': lang,
'results': {},
'errors': []
} }
if max_prob is not None:
result = f"The image is recognized as '{translator[max_class]}' with a probability of {round(max_prob * 100, 2)}%"
else:
result = f"The image is not recognized as a class within the range 281-285 ({max_class})"
return jsonify({'result': result}), 200 # Generate results
for file in files:
predictions = detect_cat(file, list_of_labels)
if predictions is not None:
predictions, error_messages = translate(predictions, lang)
results['results'][file.filename] = {
'isCat': False if not predictions else True,
**({'predictions': predictions} if predictions is not None else {})
}
if error_messages is not None and predictions is None:
results['errors'].append(error_messages)
# Send response with 200 (Success)
return Response(json.dumps(results), status=200, mimetype='application/json')
if __name__ == '__main__': if __name__ == '__main__':

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resources/en.properties Normal file
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# EN
lynx=lynx
lion=lion
tiger=tiger
cheetah=cheetah
leopard=leopard
jaguar=jaguar
tabby=tabby
Egyptian_cat=Egyptian cat
cougar=cougar
Persian_cat=Persian cat
Siamese_cat=Siamese cat
snow_leopard=snow leopard
tiger_cat=tiger cat

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resources/pl.properties Normal file
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# PL
lynx=ryś
lion=lew
tiger=tygrys
cheetah=gepard
leopard=lampart
jaguar=jaguar
tabby=kot pręgowany
Egyptian_cat=kot egipski
cougar=puma
Persian_cat=kot perski
Siamese_cat=kot syjamski
snow_leopard=lampart śnieżny
tiger_cat=kot tygrysi

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validator.py Normal file
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import imghdr
"""
Validation method.
If everything fine then returns empty list.
Else returns list of error messages.
"""
# Allowed extensions
allowed_extensions = {'jpg', 'jpeg', 'png'}
def validate(request):
errors = []
try:
images = request.files.getlist('image')
# Case 1 - > request has no 'Image' Key in body
if images is None:
raise KeyError("'Image' key not found in request.")
# Case 2 - > if some of the images has no filename
if not images or all(img.filename == '' for img in images):
raise ValueError("Value of 'Image' key is empty.")
# Case 3 -> if some of the images has wrong extension
for img in images:
if imghdr.what(img) not in allowed_extensions:
raise ValueError(f"Given file '{img.filename}' has no allowed extension. "
f"Allowed extensions: {allowed_extensions}.")
except Exception as e:
errors.append(e.args[0])
return errors