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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() |