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

#global prediction_endpoint
#global prediction_key
#global project_id
#global model_name

 # 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="Object detection confidence level"),
                title=title,
             )

# Launch the interface
interface.launch(share=True)