Create main.py

main.py

@@ -0,0 +1,66 @@
+from flask import *
+from flask import Flask
+from google.cloud import aiplatform
+from google.cloud.aiplatform.gapic.schema import predict
+import base64
+
+app = Flask(__name__)
+
+def predict_image_classification_sample(
+    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=0.5, 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
+    )
+    # 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:
+        return dict(prediction)['displayNames']
+
+# [END aiplatform_predict_image_classification_sample]
+
+@app.route("/")
+def main():
+    return render_template("form.html")
+
+@app.route('/success',methods = ["POST"])
+def success():
+    if request.method == 'POST':
+        f = request.files['file']
+        f.save("image.jpg")
+        diag1 = predict_image_classification_sample(
+            project="599337888132",
+            endpoint_id="5834189016886411264",
+            location="us-central1",
+            filename="image.jpg"
+        )
+        diag = "The diagnosis is: " + diag1[0]
+        return render_template("Success.html",string_variable = diag)
+
+if __name__ == '__main__':
+    app.run(debug=True)