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# 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)
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