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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='192.168.1.7', port=5000)
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