Update app.py

app.py

@@ -1,46 +1,46 @@
-from flask import Flask, request
-from transformers import AutoModelForImageClassification
-from transformers import AutoImageProcessor
-from PIL import Image
-import torch
+# from flask import Flask, request
+# from transformers import AutoModelForImageClassification
+# from transformers import AutoImageProcessor
+# from PIL import Image
+# import torch
 
-app = Flask(__name__)
+# app = Flask(__name__)
 
-model = AutoModelForImageClassification.from_pretrained(
-    './myModel')
-image_processor = AutoImageProcessor.from_pretrained(
-    "google/vit-base-patch16-224-in21k")
+# 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']
+# @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)
+#     # 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
-    # ...
+#     # You can perform additional operations with the image here
+#     # ...
 
-    return 'Image uploaded successfully'
+#     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")
+# @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
+#     with torch.no_grad():
+#         logits = model(**inputs).logits
 
-    predicted_label = logits.argmax(-1).item()
+#     predicted_label = logits.argmax(-1).item()
 
-    disease = model.config.id2label[predicted_label]
+#     disease = model.config.id2label[predicted_label]
 
-    return disease
+#     return disease
 
 
-if __name__ == '__app__':
-    app.run( host='192.168.1.1',port=8080)
+# if __name__ == '__app__':
+#     app.run( host='192.168.1.1',port=8080)