# from flask import Flask, request
# from transformers import AutoModelForImageClassification
# from transformers import AutoImageProcessor
# from PIL import Image
# import torch

# app = Flask(__name__)

# model = AutoModelForImageClassification.from_pretrained(
#     './myModel')
# image_processor = AutoImageProcessor.from_pretrained(
#     "google/vit-base-patch16-224-in21k")


# @app.route('/upload_image', methods=['POST'])
# def upload_image():
#     # Get the image file from the request
#     image_file = request.files['image']

#     # Save the image file to a desired location on the server
#     image_path = "assets/img.jpg"
#     image_file.save(image_path)

#     # You can perform additional operations with the image here
#     # ...

#     return 'Image uploaded successfully'


# @app.route('/get_text', methods=['GET'])
# def get_text():
#     image = Image.open('assets/img.jpg')
#     inputs = image_processor(image, return_tensors="pt")

#     with torch.no_grad():
#         logits = model(**inputs).logits

#     predicted_label = logits.argmax(-1).item()

#     disease = model.config.id2label[predicted_label]

#     return disease


# if __name__ == '__app__':
#     app.run( host='192.168.1.1',port=8080)