gooders.py

# -*- coding: utf-8 -*-
"""Gooders.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1eJ1eacIbH5Tg9sYXMxEyPi3GIOm3nE-Z
"""

!pip install --upgrade google-cloud-aiplatform

from google.colab import auth
auth.authenticate_user()

import base64

from google.cloud import aiplatform
from google.cloud.aiplatform.gapic.schema import predict

def predict_image_classification(project: str, endpoint_id: str, filename: str, location: str = "us-central1", api_endpoint: str = "us-central1-aiplatform.googleapis.com"):
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)

    with open(filename, "rb") as f:
        file_content = f.read()

    # Encode the file content to base64
    encoded_content = base64.b64encode(file_content).decode("utf-8")
    instance = predict.instance.ImageClassificationPredictionInstance(content=encoded_content).to_value()
    instances = [instance]
    parameters = predict.params.ImageClassificationPredictionParams(confidence_threshold=0, max_predictions=5).to_value()
    endpoint = client.endpoint_path(project=project, location=location, endpoint=endpoint_id)

    response = client.predict(endpoint=endpoint, instances=instances, parameters=parameters)

    # Iterate over and print out all predictions
    for prediction_result in response.predictions:
        prediction_dict = dict(prediction_result)
        print("Predicted labels and their confidence scores:")
        for label, score in prediction_dict.items():
            print(f"{label}: {score}")

# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# [START aiplatform_predict_image_classification_sample]
import base64

from google.cloud import aiplatform
from google.cloud.aiplatform.gapic.schema import predict

def predict_image_classification(
    project: str,
    endpoint_id: str,
    filename: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):

    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)
    with open(filename, "rb") as f:
        file_content = f.read()

    # The format of each instance should conform to the deployed model's prediction input schema.
    encoded_content = base64.b64encode(file_content).decode("utf-8")
    instance = predict.instance.ImageClassificationPredictionInstance(
        content=encoded_content,
    ).to_value()
    instances = [instance]
    # See gs://google-cloud-aiplatform/schema/predict/params/image_classification_1.0.0.yaml for the format of the parameters.
    parameters = predict.params.ImageClassificationPredictionParams(
        confidence_threshold=0,
        max_predictions=5,
    ).to_value()
    endpoint = client.endpoint_path(
        project=project, location=location, endpoint=endpoint_id
    )
    response = client.predict(
        endpoint=endpoint, instances=instances, parameters=parameters
    )

    print("response")
    print(" deployed_model_id:", response.deployed_model_id)
    # See gs://google-cloud-aiplatform/schema/predict/prediction/image_classification_1.0.0.yaml for the format of the predictions.
    predictions = response.predictions
    for prediction in predictions:
        pre_dict = dict(prediction)
    return pre_dict
# [END aiplatform_predict_image_classification_sample]

uploaded = files.upload()
image_path = next(iter(uploaded))
response = predict_image_classification(
    project="gooders-416303",
    endpoint_id="7422699645022765056",
    location="us-central1",
    filename=image_path
)
print(response)

# Weights for each label category (high severity has low weight and vice versa)
weights = {
    'high': 1,
    'med': 2,
    'low': 3,
    'Good': 4
}

# Calculate the total weighted score
total_weighted_score = sum(confidence * weights.get(name.split('_')[-1], 0)
                           for name, confidence in zip(response['displayNames'], response['confidences']))

# Calculate the total possible score, which is the sum of all confidences multiplied by the highest weight
total_possible_score = sum(response['confidences']) * max(weights.values())

# Calculate the quality score as a percentage of the total possible score
quality_score_percentage = (total_weighted_score / total_possible_score) * 100

print("Gooder's Rating:",quality_score_percentage,"%")