ImageClassifier/app.py at main · acbikash13/ImageClassifier · GitHub

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from flask import Flask, render_template , request, jsonify
from PIL import  Image
import tensorflow as tf
import numpy as np
import tensorflow as tf
import os
from azure.storage.blob import BlobServiceClient

app = Flask(__name__)


def download_model_from_azure_blob():
    # azure storage account connection string
    connection_string = "DefaultEndpointsProtocol=https;AccountName=appjob;AccountKey=QBrjqwJvqjvX5d2GIHjArBAoUdyvYz4jCfZfm6qq7ih8meagA4ABvepEi3MoYbNhOhhm8aZJfn8Z+AStAiJ0ow==;EndpointSuffix=core.windows.net"
    container_name = 'imagedetectioncontainer'
    blob_name = 'saved_model.h5'
    # make a blob servicec client
    blob_service_client = BlobServiceClient.from_connection_string(connection_string)
    blob_client = blob_service_client.get_blob_client(container=container_name, blob=blob_name)
    # Local path where the model will be saved
    local_path = os.path.join(os.getcwd(), blob_name)
    print("The local path si " + local_path)

    # Download the model from Azure Blob Storage
    with open(local_path, "wb") as download_file:
        download_file.write(blob_client.download_blob().readall())
        print("The model file is downloaded.")
    return local_path


def load_model():
     #saved model path
    print("Entering in the load model")
    print("Current directory is " +  os.getcwd())
    model_path = download_model_from_azure_blob()
    model = tf.keras.models.load_model(model_path,custom_objects=None , safe_mode = False)
    print("Model Successfully loaded...")
    return model


def preprocess_image(image):
    # resize the image
    image_width = 264
    image_heigth = 264
    image =  image.resize((image_heigth,image_width))
    #convert the image into numpy array
    image_array = np.array(image)

    #Normalize the image array
    image_array = image_array / 255.0

    # Add batch dimension
    image_array = np.expand_dims(image_array, axis=0)
    return image_array

model =  load_model()

@app.route('/')
def homepage():  # put application's code here
    return render_template('homepage.html')


@app.route('/predict', methods=['POST'])
def predictImage():
    # check if the file exists or not
    if 'file' not in request.files:
        return jsonify({"Error!" : "No image received. Please try again! "}), 400


    # Get the image file from the request
    image_file = request.files['file']
    # Open the image file
    image = Image.open(image_file.stream)
    # Preprocess the image
    input_data = preprocess_image(image)

    # Convert to TensorFlow tensor
    input_tensor = tf.convert_to_tensor(input_data)

    # Make predictions using the model
    predictions = model(input_tensor)

    class_names = ['Buildings', 'Forest', 'Glacier', 'Mountain', 'Sea', 'Street']
    # Assuming your model outputs probabilities for classes, get the predicted class
    predicted_class = class_names[np.argmax(predictions.numpy(), axis=1)[0]]
    print("The predicted class is ", predicted_class)
    # Return the predicted class as JSON
    return jsonify(predicted_class = predicted_class)


if __name__ == '__main__':
    print("Starting the server")
    app.run()