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# 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_custom_trained_model_sample] | |
from typing import Dict, List, Union | |
from google.cloud import aiplatform | |
from google.protobuf import json_format | |
from google.protobuf.struct_pb2 import Value | |
import os | |
content = os.environ['API_KEY'] | |
with open('key.json', 'w') as file: | |
file.write(content) | |
os.environ['GOOGLE_APPLICATION_CREDENTIALS']= './key.json' | |
def predict_custom_trained_model_sample( | |
project: str, | |
endpoint_id: str, | |
instances: Union[Dict, List[Dict]], | |
location: str = "us-central1", | |
api_endpoint: str = "us-central1-aiplatform.googleapis.com", | |
): | |
""" | |
`instances` can be either single instance of type dict or a list | |
of instances. | |
""" | |
# 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) | |
# The format of each instance should conform to the deployed model's prediction input schema. | |
instances = instances if type(instances) == list else [instances] | |
instances = [ | |
json_format.ParseDict(instance_dict, Value()) for instance_dict in instances | |
] | |
parameters_dict = {} | |
parameters = json_format.ParseDict(parameters_dict, 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) | |
# The predictions are a google.protobuf.Value representation of the model's predictions. | |
predictions = response.predictions | |
for prediction in predictions: | |
print(" prediction:", dict(prediction)) | |
return predictions[0] | |
# [END aiplatform_predict_custom_trained_model_sample] | |
import base64 | |
import os | |
from datetime import datetime | |
from io import BytesIO | |
import numpy as np | |
import requests | |
from google.cloud import aiplatform | |
from PIL import Image | |
def download_image(url): | |
response = requests.get(url) | |
return Image.open(BytesIO(response.content)).convert("RGB") | |
def image_to_base64(image, format="JPEG"): | |
# Convert numpy array to PIL Image | |
image_pil = Image.fromarray((image * 255).astype(np.uint8)) | |
buffer = BytesIO() | |
image_pil.save(buffer, format=format) | |
image_str = base64.b64encode(buffer.getvalue()).decode("utf-8") | |
return image_str | |
import gradio as gr | |
def predict (image, text): | |
if len(text) == 0: | |
return "No prompt provided" | |
response = predict_custom_trained_model_sample( | |
instances=[{ "image": image_to_base64(image),"text":text}], | |
project="1018963165306", | |
endpoint_id="5638185676072550400", | |
location="us-central1" | |
) | |
print(dict(response)) | |
return dict(response)['answer'] | |
demo = gr.Interface(fn=predict, inputs=["image","text"],outputs="text") | |
demo.launch() |