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Update app.py
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app.py
CHANGED
@@ -8,6 +8,7 @@ def load_models():
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feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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return model, feature_extractor, tokenizer
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#pickle.load(open('energy_model.pkl', 'rb'))
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#vocab = np.load('w2i.p', allow_pickle=True)
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st.title("Image_Captioning_App")
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@@ -24,9 +25,9 @@ def load_image(img):
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return im
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uploaded_photo = c2.file_uploader("Upload Image",type=['jpg','png','jpeg'], on_change=change_photo_state)
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camera_photo = c2.camera_input("Take a photo", on_change=change_photo_state)
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#st.subheader("Detection")
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if st.checkbox("Generate_Caption"):
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model, feature_extractor, tokenizer = load_models()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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max_length = 16
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feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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return model, feature_extractor, tokenizer
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model, feature_extractor, tokenizer = load_models()
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#pickle.load(open('energy_model.pkl', 'rb'))
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#vocab = np.load('w2i.p', allow_pickle=True)
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st.title("Image_Captioning_App")
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return im
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uploaded_photo = c2.file_uploader("Upload Image",type=['jpg','png','jpeg'], on_change=change_photo_state)
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camera_photo = c2.camera_input("Take a photo", on_change=change_photo_state)
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#st.subheader("Detection")
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if st.checkbox("Generate_Caption"):
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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max_length = 16
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