app.py

# 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()