Spaces:
Runtime error
Runtime error
File size: 4,334 Bytes
df0d440 ed25dd4 2cefd9b ed25dd4 ac92d72 df0d440 543f7b1 4acae3e e720316 4acae3e e720316 4acae3e a7f0ae3 df0d440 4acae3e ce989ee 4acae3e e720316 4acae3e 75241f1 e720316 df0d440 0c120b4 df0d440 ce989ee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
import os
import time
import numpy as np
from pathlib import Path
os.environ['TRANSFORMERS_CACHE'] = str(Path('./artifacts/').absolute())
# Disable tensorflow warnings
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from tensorflow import keras
from flask import Flask, jsonify, request
load_type = 'remote_hub_from_pretrained'
"""
local;
remote_hub_download; - /cache error even using TRANSFORMERS_CACHE & cache_dir to local folder
remote_hub_from_pretrained; - /cache error even using TRANSFORMERS_CACHE & cache_dir to local folder
remote_hub_pipeline; - needs config.json and this is not easy to grasp how to do it with custom models
https://discuss.huggingface.co/t/how-to-create-a-config-json-after-saving-a-model/10459/4
"""
REPO_ID = "1vash/mnist_demo_model"
# Load the saved model into memory
if load_type == 'local':
model = keras.models.load_model('artifacts/models/mnist_model.h5')
elif load_type == 'remote_hub_download':
from huggingface_hub import hf_hub_download
model = keras.models.load_model(hf_hub_download(repo_id=REPO_ID, filename="saved_model.pb"))
elif load_type == 'remote_hub_from_pretrained':
# https://huggingface.co/docs/hub/keras
from huggingface_hub import from_pretrained_keras
model = from_pretrained_keras(REPO_ID, cache_dir='./artifacts/')
elif load_type == 'remote_hub_pipeline':
from transformers import pipeline
classifier = pipeline("image-classification", model=REPO_ID)
else:
pass
# Initialize the Flask application
app = Flask(__name__)
# API route for prediction
@app.route('/predict', methods=['POST'])
def predict():
"""
Predicts the class label of an input image.
Request format:
{
"image": [[pixel_values_gray]]
}
Response format:
{
"prediction": predicted_label,
"ml-latency-ms": latency_in_milliseconds
(Measures time only for ML operations preprocessing with predict)
}
"""
start_time = time.time()
# Get the image data from the request
image_data = request.get_json()['image']
# Preprocess the image
processed_image = preprocess_image(image_data)
# Make a prediction, verbose=0 to disable progress bar in logs
prediction = model.predict(processed_image, verbose=0)
# Get the predicted class label
predicted_label = np.argmax(prediction)
# Calculate latency in milliseconds
latency_ms = (time.time() - start_time) * 1000
# Return the prediction result and latency as JSON response
response = {'prediction': int(predicted_label),
'ml-latency-ms': round(latency_ms, 4)}
# dictionary is not a JSON: https://www.quora.com/What-is-the-difference-between-JSON-and-a-dictionary
# flask.jsonify vs json.dumps https://sentry.io/answers/difference-between-json-dumps-and-flask-jsonify/
# The flask.jsonify() function returns a Response object with Serializable JSON and content_type=application/json.
return jsonify(response)
# Helper function to preprocess the image
def preprocess_image(image_data):
"""Preprocess image for Model Inference
:param image_data: Raw image
:return: image: Preprocessed Image
"""
# Resize the image to match the input shape of the model
image = np.array(image_data).reshape(1, 28, 28)
# Normalize the pixel values
image = image.astype('float32') / 255.0
return image
# API route for health check
@app.route('/health', methods=['GET'])
def health():
"""
Health check API to ensure the application is running.
Returns "OK" if the application is healthy.
Demo Usage: "curl http://localhost:5000/health" or using alias "curl http://127.0.0.1:5000/health"
"""
return 'OK'
# API route for version
@app.route('/version', methods=['GET'])
def version():
"""
Returns the version of the application.
Demo Usage: "curl http://127.0.0.1:5000/version" or using alias "curl http://127.0.0.1:5000/version"
"""
return '1.0'
@app.route("/")
def hello_world():
return "<p>Hello, Team!</p>"
# Start the Flask application
if __name__ == '__main__':
app.run()
##################
# Flask API usages:
# 1. Just a wrapper over OpenAI API
# 2. You can use Chain calls of OpenAI API
# 3. Using your own ML model in combination with openAPI functionality
# 4. ...
##################
|