Upload app.py

app.py

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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(
+    '../apiModel/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)