from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
from PIL import Image
styles = ["Adenocarcinoma","Large cell carcinoma","normal","squamous cell carcinoma"
]
import gradio as gr
def preprocess(img):
  new_img = Image.fromarray((img * 255).astype('uint8'))
  new_img = new_img.resize((224, 224))
  model = load_model("chest.h5")
  new_img = image.img_to_array(new_img)
  new_img = np.expand_dims(new_img, axis=0)
  pred = model.predict(new_img)
  return styles[np.argmax(pred)]

iface = gr.Interface(
    fn = preprocess,  # Function to call on user input
    inputs = gr.Image(),  # Input type (image upload)
    outputs = "text",  # Output type (text label)
    live=True,
    title="Image Classification made by Siddhant Bashisth",
    description="Upload the image",
)

# Launch the Gradio app
iface.launch(debug = True)