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from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
from msrest.authentication import ApiKeyCredentials
from dotenv import load_dotenv
from PIL import Image
import os
import gradio as gr
from io import BytesIO
# Get Configuration Settings
load_dotenv()
prediction_endpoint = os.getenv('PredictionEndpoint')
prediction_key = os.getenv('PredictionKey')
project_id = os.getenv('ProjectID')
model_name = os.getenv('ModelName')
def classifyImage(image):
try:
# Convert PIL Image to bytes
image_bytes = BytesIO()
image.save(image_bytes, format='JPEG')
image_bytes = image_bytes.getvalue()
# Authenticate a client for the training API
credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key})
prediction_client = CustomVisionPredictionClient(endpoint=prediction_endpoint, credentials=credentials)
# Classify test images
results = prediction_client.classify_image(project_id, model_name, image_bytes)
# Loop over each label prediction and print any with probability > 50%
for prediction in results.predictions:
if prediction.probability > 0.5:
summary = ('{} ({:.0%})'.format(prediction.tag_name, prediction.probability))
return summary
except Exception as ex:
return None, f"Error: {str(ex)}"
title = "Detect 3 types of fruits - Carrot, Cucumber, Pear"
interface = gr.Interface(
fn=classifyImage,
inputs=gr.Image(type="pil", label="Input Image"),
outputs=gr.Textbox(label="Fruit detection with confidence level"),
title=title,
)
# Launch the interface
interface.launch(share=True)
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