Update app.py
Browse files
app.py
CHANGED
@@ -75,32 +75,41 @@ model.eval()
|
|
75 |
|
76 |
# Image upload
|
77 |
uploaded_img = st.file_uploader("**Upload an image**", type=["jpg", "jpeg", "png"])
|
78 |
-
|
79 |
-
if uploaded_img is not None:
|
80 |
-
|
81 |
-
|
82 |
-
|
83 |
-
|
84 |
-
|
85 |
-
|
86 |
-
|
87 |
-
|
88 |
-
|
89 |
-
|
90 |
-
|
91 |
-
|
92 |
-
|
93 |
-
|
94 |
-
# Model inference
|
95 |
-
with torch.no_grad():
|
96 |
-
pred = model(transformed_img).argmax(dim=1).item()
|
97 |
|
98 |
-
|
99 |
-
|
100 |
-
|
101 |
-
|
102 |
-
|
103 |
-
|
104 |
-
|
105 |
-
|
106 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
75 |
|
76 |
# Image upload
|
77 |
uploaded_img = st.file_uploader("**Upload an image**", type=["jpg", "jpeg", "png"])
|
78 |
+
if st.button("Submit"):
|
79 |
+
if uploaded_img is not None:
|
80 |
+
# Display uploaded image in a smaller size
|
81 |
+
image = Image.open(uploaded_img)
|
82 |
+
st.image(image, caption="**Uploaded Image**", width=200)
|
83 |
+
|
84 |
+
# Image transformations
|
85 |
+
sample_transform = transforms.Compose([
|
86 |
+
transforms.Resize((224, 224)),
|
87 |
+
transforms.ToTensor(),
|
88 |
+
transforms.Normalize(mean=[0.1776, 0.1776, 0.1776], std=[0.1735, 0.1735, 0.1735])
|
89 |
+
])
|
90 |
+
|
91 |
+
# Apply transformations
|
92 |
+
transformed_img = sample_transform(image).unsqueeze(0)
|
|
|
|
|
|
|
|
|
93 |
|
94 |
+
# Model inference
|
95 |
+
with torch.no_grad():
|
96 |
+
pred = model(transformed_img).argmax(dim=1).item()
|
97 |
+
|
98 |
+
# Stylish output box
|
99 |
+
st.markdown(
|
100 |
+
f"""
|
101 |
+
<div class="output-container">
|
102 |
+
🧠 <strong>Predicted Class:</strong> {class_names[pred]}
|
103 |
+
</div>
|
104 |
+
""",
|
105 |
+
unsafe_allow_html=True
|
106 |
+
)
|
107 |
+
else:
|
108 |
+
st.markdown(
|
109 |
+
"""
|
110 |
+
<div style='background-color: #f8d7da; padding: 10px; border-radius: 5px;'>
|
111 |
+
<h4 style='color: #721c24;'> ⚠️ Plese upload image </h4>
|
112 |
+
</div>
|
113 |
+
""",
|
114 |
+
unsafe_allow_html=True
|
115 |
+
)
|