Add app file

app.py

@@ -0,0 +1,104 @@
+# 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
+os.environ['GOOGLE_APPLICATION_CREDENTIALS']='./key-int.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(share=True)