UnboundLocalError: cannot access local variable 'image_bytes' where it is not associated with a value

Question

quite new to the programming world and trying to create a Streamlit web app for image classification (GCP AutoML, single-label model, hosted in a GS bucket). I got the following error message; complete code follows below. Your input is appreciated.

UnboundLocalError: cannot access local variable 'image_bytes' where it is not associated with a value

Traceback: File "C:\Python\Python311\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 534, in _run_script exec(code, module.dict) File "D:\OneDrive - XXXXXXX\Desktop\Python\appcopyv4.py", line 66, in predicted_class_name, predicted_class_confidence = predict_image(image_bytes, model_id) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\OneDrive - XXXXXXX\Desktop\Python\appcopyv4.py", line 22, in predict_image image = np.frombuffer(image_bytes, dtype=np.uint8)

import streamlit as st
from PIL import Image
import numpy as np
from google.cloud import automl_v1beta1
from google.cloud import automl as automl_v1beta1; ImagePayload = automl_v1beta1
from google.cloud.automl_v1beta1 import PredictionServiceClient
from google.oauth2 import service_account

Replace with your project ID and model ID

keyfile_path = 'D:\OneDrive - XXXXXXX\Desktop\Python\xxxxxxxxxxxxx-123456-122s3df4455563.json'  # Replace with your key file path
PROJECT_ID = " xxxxxxxxxxxxx-123456"
model_id = "123456798123456789"

Create a client for the AutoML API

client = automl_v1beta1.AutoMlClient.from_service_account_file(keyfile_path)
location = f'https://storage.cloud.google.com/[bucket name]/model-123456798123456789'  # Replace with your model's location

Define the function to make predictions using the AutoML model

def predict_image(image, model_id):
    # Convert the PIL image to a NumPy array
    image = np.frombuffer(image_bytes, dtype=np.uint8)

# Convert the image to bytes based on format
    if image.dtype == np.uint8:  # JPG or JPEG
        image_bytes = image.tobytes()
    elif image.dtype == np.uint16:  # PNG
        image_bytes = image.astype(np.uint8).tobytes()

    else:
        raise Exception("Unsupported image format")

# Create the prediction request
    prediction_request = automl_v1beta1.PredictRequest(
        name=f"projects/{PROJECT_ID}/locations/us-central1/models/{model_id}",
        image_payload=ImagePayload(image=image_bytes),
)

# Send the request and get the response
    prediction_response = client.predict(prediction_request)

# Get the predicted class and confidence score
    predicted_class_name = prediction_response.payload[0].classification.top_1_label
    predicted_class_confidence = prediction_response.payload[0].classification.top_1_score

    return predicted_class_name, predicted_class_confidence

Initialize the Streamlit app

st.title("Image Classification with AutoML")

Upload an image file

uploaded_file = st.file_uploader("Choose an image...", type=["JPG", "JPEG", "PNG"])

Check if an image is uploaded

if uploaded_file is not None:
    # Read the uploaded image
    image = Image.open(uploaded_file)

# Show the uploaded image
    st.image(image, caption="Uploaded Image")

# Extract the image bytes from the uploaded file
    image_bytes = uploaded_file.read()

# Pass the image bytes and model ID to the predict_image function
    predicted_class_name, predicted_class_confidence = predict_image(image_bytes, model_id)


# Display the prediction
    st.write(f"Predicted class: {predicted_class_name}")
    st.write(f"Confidence score: {predicted_class_confidence:.2f}")

Thanks for your feedback!