import gradio as gr
from PIL import Image
from transformers import AutoFeatureExtractor, AutoModelForImageSegmentation
import tensorflow as tf

extractor = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic")
model = AutoModelForImageSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")


# Perform image classification for single class output
# def predict_class(image):
#     img = tf.cast(image, tf.float32)
#     img = tf.image.resize(img, [input_shape[0], input_shape[1]])
#     img = tf.expand_dims(img, axis=0)
#     prediction = model.predict(img)
#     class_index = tf.argmax(prediction[0]).numpy()
#     predicted_class = labels[class_index]
#     return predicted_class

# Perform image classification for multy class output
def predict_class(image):
    img = tf.cast(image, tf.float32)
    prediction = model.predict(img)
    return prediction

# UI Design for single class output
# def classify_image(image):
#     predicted_class = predict_class(image)
#     output = f"<h2>Predicted Class: <span style='text-transform:uppercase';>{predicted_class}</span></h2>"
#     return output


# UI Design for multy class output
def classify_image(image):
    results = predict_class(image)
    
    return results


inputs = gr.inputs.Image(type="pil", label="Upload an image")
# outputs = gr.outputs.HTML() #uncomment for single class output 
outputs = gr.outputs.Label(num_top_classes=4)

title = "<h1 style='text-align: center;'>Image Classifier</h1>"
description = "Upload an image and get the predicted class."
# css_code='body{background-image:url("file=wave.mp4");}'

gr.Interface(fn=classify_image, 
             inputs=inputs, 
             outputs=outputs, 
             title=title, 
             examples=[["00_plane.jpg"], ["01_car.jpg"], ["02_bird.jpg"], ["03_cat.jpg"], ["04_deer.jpg"]],
             # css=css_code,
             description=description).launch()