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import io |
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import os |
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import streamlit as st |
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import requests |
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from PIL import Image |
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from model import get_caption_model, generate_caption |
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@st.cache(allow_output_mutation=True) |
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def get_model(): |
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return get_caption_model() |
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caption_model = get_model() |
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def predict(): |
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captions = [] |
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pred_caption = generate_caption('tmp.jpg', caption_model) |
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st.markdown('#### Predicted Captions:') |
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captions.append(pred_caption) |
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for _ in range(4): |
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pred_caption = generate_caption('tmp.jpg', caption_model, add_noise=True) |
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if pred_caption not in captions: |
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captions.append(pred_caption) |
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for c in captions: |
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st.write(c) |
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st.title('Bone Fracture Detection') |
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img_url = st.text_input(label='Enter Image URL') |
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if (img_url != "") and (img_url != None): |
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img = Image.open(requests.get(img_url, stream=True).raw) |
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img = img.convert('RGB') |
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st.image(img) |
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img.save('tmp.jpg') |
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predict() |
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os.remove('tmp.jpg') |
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st.markdown('<center style="opacity: 70%">OR</center>', unsafe_allow_html=True) |
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img_upload = st.file_uploader(label='Upload Image', type=['jpg', 'png', 'jpeg']) |
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if img_upload != None: |
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img = img_upload.read() |
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img = Image.open(io.BytesIO(img)) |
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img = img.convert('RGB') |
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img.save('tmp.jpg') |
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st.image(img) |
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predict() |
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os.remove('tmp.jpg') |
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