Create app.py

app.py

@@ -0,0 +1,53 @@
+import io
+import os
+import streamlit as st
+import requests
+from PIL import Image
+from model import bone_frac
+
+
+@st.cache(allow_output_mutation=True)
+# def get_model():
+#     return get_caption_model()
+
+# caption_model = get_model()
+pred_model=bone_frac()
+
+# def predict():
+#     captions = []
+#     pred_caption = generate_caption('tmp.jpg', caption_model)
+
+#     st.markdown('#### Predicted Captions:')
+#     captions.append(pred_caption)
+
+#     for _ in range(4):
+#         pred_caption = generate_caption('tmp.jpg', caption_model, add_noise=True)
+#         if pred_caption not in captions:
+#             captions.append(pred_caption)
+    
+#     for c in captions:
+        # st.write(c)
+
+st.title('Bone Fracture Detection')
+img_url = st.text_input(label='Enter Image URL')
+
+if (img_url != "") and (img_url != None):
+    img = Image.open(requests.get(img_url, stream=True).raw)
+    img = img.convert('RGB')
+    st.image(img)
+    img.save('tmp.jpg')
+    predict()
+    os.remove('tmp.jpg')
+
+
+st.markdown('<center style="opacity: 70%">OR</center>', unsafe_allow_html=True)
+img_upload = st.file_uploader(label='Upload Image', type=['jpg', 'png', 'jpeg'])
+
+if img_upload != None:
+    img = img_upload.read()
+    img = Image.open(io.BytesIO(img))
+    img = img.convert('RGB')
+    img.save('tmp.jpg')
+    st.image(img)
+    st.write(pred_model(img))
+    os.remove('tmp.jpg')