| # 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) | |