Spaces:
Runtime error
Runtime error
Commit
·
bfb8cb9
1
Parent(s):
d3a3d62
:lipstick: style
Browse files
app.py
CHANGED
@@ -17,6 +17,7 @@ COLORS = [
|
|
17 |
[0.301, 0.745, 0.933],
|
18 |
]
|
19 |
|
|
|
20 |
@st.cache(allow_output_mutation=True)
|
21 |
def get_hf_components(model_name_or_path):
|
22 |
feature_extractor = DetrFeatureExtractor.from_pretrained(model_name_or_path)
|
@@ -24,10 +25,12 @@ def get_hf_components(model_name_or_path):
|
|
24 |
model.eval()
|
25 |
return feature_extractor, model
|
26 |
|
|
|
27 |
@st.cache
|
28 |
def get_img_from_url(url):
|
29 |
return Image.open(requests.get(url, stream=True).raw)
|
30 |
|
|
|
31 |
def fig2img(fig):
|
32 |
buf = io.BytesIO()
|
33 |
fig.savefig(buf)
|
@@ -54,6 +57,7 @@ def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
|
|
54 |
plt.axis("off")
|
55 |
return fig2img(plt.gcf())
|
56 |
|
|
|
57 |
def make_prediction(img, feature_extractor, model):
|
58 |
inputs = feature_extractor(img, return_tensors="pt")
|
59 |
outputs = model(**inputs)
|
@@ -61,6 +65,7 @@ def make_prediction(img, feature_extractor, model):
|
|
61 |
processed_outputs = feature_extractor.post_process(outputs, img_size)
|
62 |
return processed_outputs[0]
|
63 |
|
|
|
64 |
def main():
|
65 |
option = st.selectbox("Which model should we use?", ("facebook/detr-resnet-50", "facebook/detr-resnet-101"))
|
66 |
feature_extractor, model = get_hf_components(option)
|
@@ -72,5 +77,5 @@ def main():
|
|
72 |
st.image(viz_img)
|
73 |
|
74 |
|
75 |
-
if __name__ ==
|
76 |
main()
|
|
|
17 |
[0.301, 0.745, 0.933],
|
18 |
]
|
19 |
|
20 |
+
|
21 |
@st.cache(allow_output_mutation=True)
|
22 |
def get_hf_components(model_name_or_path):
|
23 |
feature_extractor = DetrFeatureExtractor.from_pretrained(model_name_or_path)
|
|
|
25 |
model.eval()
|
26 |
return feature_extractor, model
|
27 |
|
28 |
+
|
29 |
@st.cache
|
30 |
def get_img_from_url(url):
|
31 |
return Image.open(requests.get(url, stream=True).raw)
|
32 |
|
33 |
+
|
34 |
def fig2img(fig):
|
35 |
buf = io.BytesIO()
|
36 |
fig.savefig(buf)
|
|
|
57 |
plt.axis("off")
|
58 |
return fig2img(plt.gcf())
|
59 |
|
60 |
+
|
61 |
def make_prediction(img, feature_extractor, model):
|
62 |
inputs = feature_extractor(img, return_tensors="pt")
|
63 |
outputs = model(**inputs)
|
|
|
65 |
processed_outputs = feature_extractor.post_process(outputs, img_size)
|
66 |
return processed_outputs[0]
|
67 |
|
68 |
+
|
69 |
def main():
|
70 |
option = st.selectbox("Which model should we use?", ("facebook/detr-resnet-50", "facebook/detr-resnet-101"))
|
71 |
feature_extractor, model = get_hf_components(option)
|
|
|
77 |
st.image(viz_img)
|
78 |
|
79 |
|
80 |
+
if __name__ == "__main__":
|
81 |
main()
|