52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
# For executing: python3 app.py /path/to/file
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# Import libraries
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from PIL import Image, UnidentifiedImageError
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from transformers import ViTImageProcessor, ViTForImageClassification
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import sys
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# Specify the local directory where the model files are stored
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local_model_path = '/home/overnion/Status200/models/pretrained'
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# Check if the image path is provided
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if len(sys.argv) < 2:
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print("Error: No image path provided. Please provide the path to the image as an argument.")
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exit()
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# Load the image processor and model
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model_name = 'vishnun0027/Crop_Disease_model_1'
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image_processor = ViTImageProcessor.from_pretrained(model_name)
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model = ViTForImageClassification.from_pretrained(
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model_name,
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ignore_mismatched_sizes=True
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)
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# Load image
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image_path = sys.argv[1] # Get the image path from command line arguments
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try:
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image = Image.open(image_path)
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# Convert the image to RGB if it's not already
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if image.mode != 'RGB':
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image = image.convert('RGB')
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except FileNotFoundError:
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print("Error: Image file not found.")
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exit()
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except UnidentifiedImageError:
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print("Error: Unable to open image. Check the file type.")
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exit()
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except Exception as e:
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print(f"An error occurred: {e}")
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exit()
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# Preparing the image for the model
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inputs = image_processor(images=image, return_tensors="pt")
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# Make the prediction
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outputs = model(**inputs)
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logits = outputs.logits
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predicted_class_idx = logits.argmax(-1).item()
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# Print the predicted class
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print(model.config.id2label[predicted_class_idx])
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