Anwarkh1 commited on
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9adb9c8
1 Parent(s): ce3c4cd

Update main.py

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Files changed (1) hide show
  1. main.py +3 -10
main.py CHANGED
@@ -2,7 +2,7 @@ import os
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  os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface_cache" # Set cache directory to a writable location
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  from fastapi import FastAPI, UploadFile, File
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- from transformers import ViTForImageClassification, ViTFeatureExtractor
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  import torch
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  import torch.nn as nn
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  import torchvision.transforms as transforms
@@ -12,16 +12,9 @@ import io
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  app = FastAPI()
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  # Load the ViT model and its feature extractor
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- model_name = "google/vit-base-patch16-224-in21k"
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  model = ViTForImageClassification.from_pretrained(model_name)
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- feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
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-
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- # Load the trained model weights
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- num_classes = 7
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- model.classifier = nn.Linear(model.config.hidden_size, num_classes)
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- # Load the trained weights
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- model.load_state_dict(torch.load("models/Anwarkh1/Skin_Cancer-Image_Classification", map_location=torch.device('cpu')))
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- model.eval()
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  # Define class labels
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  class_labels = ['benign_keratosis-like_lesions', 'basal_cell_carcinoma', 'actinic_keratoses', 'vascular_lesions', 'melanocytic_Nevi', 'melanoma', 'dermatofibroma']
 
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  os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface_cache" # Set cache directory to a writable location
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  from fastapi import FastAPI, UploadFile, File
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+ from transformers import ViTForImageClassification, ViTFeatureExtractor, AutoTokenizer
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  import torch
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  import torch.nn as nn
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  import torchvision.transforms as transforms
 
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  app = FastAPI()
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  # Load the ViT model and its feature extractor
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+ model_name = "Anwarkh1/Skin_Cancer-Image_Classification"
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  model = ViTForImageClassification.from_pretrained(model_name)
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 
 
 
 
 
 
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  # Define class labels
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  class_labels = ['benign_keratosis-like_lesions', 'basal_cell_carcinoma', 'actinic_keratoses', 'vascular_lesions', 'melanocytic_Nevi', 'melanoma', 'dermatofibroma']