Spaces:
Sleeping
Sleeping
Update app.py
Browse files
app.py
CHANGED
@@ -6,55 +6,130 @@ import requests
|
|
6 |
from io import BytesIO
|
7 |
from fastai.vision.all import load_learner
|
8 |
|
9 |
-
# Initialize Streamlit app
|
10 |
-
st.title("White Blood Cell Classifier")
|
11 |
|
12 |
-
|
13 |
-
st.markdown("""
|
14 |
-
This app allows you to classify white blood cells from an uploaded image.
|
15 |
-
You can upload an image of a blood sample, and the app will predict the type of white blood cell present.
|
16 |
-
Choose from various cell types like eosinophil, lymphocyte, monocyte, and neutrophil.
|
17 |
-
Note: To get the best results, please make sure there is only one WBC in the image. This model has not been trained on basophils.
|
18 |
-
""")
|
19 |
-
|
20 |
-
# Load the FastAI model for WBC identification
|
21 |
-
fastai_model = load_learner('model1.pkl')
|
22 |
-
|
23 |
-
# File uploader for image input
|
24 |
-
uploaded_file = st.file_uploader("Upload an image for classification", type=["jpg", "png"])
|
25 |
-
|
26 |
-
if uploaded_file:
|
27 |
-
# Open the uploaded image
|
28 |
-
image = Image.open(uploaded_file).convert('RGB')
|
29 |
|
|
|
|
|
|
|
30 |
|
31 |
-
#
|
32 |
-
st.
|
33 |
-
|
34 |
-
|
35 |
-
|
36 |
-
|
37 |
-
|
38 |
-
|
39 |
-
|
40 |
-
|
41 |
-
|
42 |
-
|
43 |
-
|
44 |
-
|
45 |
-
|
46 |
-
|
47 |
-
|
48 |
-
|
49 |
-
|
50 |
-
|
51 |
-
|
52 |
-
|
53 |
-
|
54 |
-
|
55 |
-
|
56 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
57 |
|
58 |
-
|
59 |
-
|
60 |
-
st.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
6 |
from io import BytesIO
|
7 |
from fastai.vision.all import load_learner
|
8 |
|
|
|
|
|
9 |
|
10 |
+
options = st.selectbox("which model you wanna choose?", ("wbc classifier", "Blood Cell Detection with YOLOv8"))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
11 |
|
12 |
+
if options == "wbc classifier":
|
13 |
+
# Initialize Streamlit app
|
14 |
+
st.title("White Blood Cell Classifier")
|
15 |
|
16 |
+
# Add a description or subtitle
|
17 |
+
st.markdown("""
|
18 |
+
This app allows you to classify white blood cells from an uploaded image.
|
19 |
+
You can upload an image of a blood sample, and the app will predict the type of white blood cell present.
|
20 |
+
Choose from various cell types like eosinophil, lymphocyte, monocyte, and neutrophil.
|
21 |
+
Note: To get the best results, please make sure there is only one WBC in the image. This model has not been trained on basophils.
|
22 |
+
""")
|
23 |
+
|
24 |
+
# Load the FastAI model for WBC identification
|
25 |
+
fastai_model = load_learner('model1.pkl')
|
26 |
+
|
27 |
+
# File uploader for image input
|
28 |
+
uploaded_file = st.file_uploader("Upload an image for classification", type=["jpg", "png"])
|
29 |
+
|
30 |
+
if uploaded_file:
|
31 |
+
# Open the uploaded image
|
32 |
+
image = Image.open(uploaded_file).convert('RGB')
|
33 |
+
|
34 |
+
|
35 |
+
# Display the uploaded image with a caption
|
36 |
+
st.image(image, caption="Reduced Size Image", use_column_width=False, width=150) # 150 pixels wide
|
37 |
+
|
38 |
+
# Perform inference with the FastAI model
|
39 |
+
pred, idx, probs = fastai_model.predict(image)
|
40 |
+
|
41 |
+
# Display a title for the results section
|
42 |
+
st.subheader("White Blood Cell Classification Results")
|
43 |
+
|
44 |
+
# Define categories for classification
|
45 |
+
categories = ('EOSINOPHIL', 'LYMPHOCYTE', 'MONOCYTE', 'NEUTROPHIL')
|
46 |
+
|
47 |
+
# Create a DataFrame with classification probabilities
|
48 |
+
results_df = pd.DataFrame(
|
49 |
+
{'Cell Type': categories, 'Probability': probs.tolist()}
|
50 |
+
)
|
51 |
+
# Highlight the most likely class
|
52 |
+
most_likely_class = categories[idx]
|
53 |
+
st.success(f"Predicted Class: {most_likely_class}")
|
54 |
+
|
55 |
+
# Additional information about the probabilities
|
56 |
+
st.write("Detailed Classification Results:")
|
57 |
+
st.table(results_df)
|
58 |
+
|
59 |
+
# Display the probabilities as a bar chart
|
60 |
+
st.bar_chart(results_df.set_index('Cell Type'))
|
61 |
+
|
62 |
+
|
63 |
+
else:
|
64 |
+
st.warning("Upload an image to start classification.")
|
65 |
|
66 |
+
if options == "Blood Cell Detection with YOLOv8":
|
67 |
+
# Initialize Streamlit app
|
68 |
+
st.title("Blood Cell Detection with YOLOv8")
|
69 |
+
|
70 |
+
# Load YOLO model
|
71 |
+
model = YOLO('keremberke/yolov8m-blood-cell-detection')
|
72 |
+
|
73 |
+
# Set model parameters
|
74 |
+
model.overrides['conf'] = 0.25 # NMS confidence threshold
|
75 |
+
model.overrides['iou'] = 0.45 # NMS IoU threshold
|
76 |
+
model.overrides['agnostic_nms'] = False # NMS class-agnostic
|
77 |
+
model.overrides['max_det'] = 1000 # Maximum number of detections per image
|
78 |
+
|
79 |
+
# Load the FastAI model for WBC identification
|
80 |
+
fastai_model = load_learner('model1.pkl')
|
81 |
+
|
82 |
+
# File uploader for image input
|
83 |
+
uploaded_file = st.file_uploader("Upload an image for detection", type=["jpg", "png"])
|
84 |
+
|
85 |
+
if uploaded_file:
|
86 |
+
# Open the uploaded image
|
87 |
+
image = Image.open(uploaded_file)
|
88 |
+
|
89 |
+
# Perform inference
|
90 |
+
results = model.predict(np.array(image))
|
91 |
+
|
92 |
+
# Display results
|
93 |
+
st.image(image, caption="Uploaded Image", use_column_width=True)
|
94 |
+
|
95 |
+
# Render detection results
|
96 |
+
rendered_image = render_result(model=model, image=image, result=results[0])
|
97 |
+
|
98 |
+
# Show the rendered result
|
99 |
+
st.image(rendered_image, caption="Detection Results", use_column_width=True)
|
100 |
+
|
101 |
+
# Count the number of each cell type
|
102 |
+
cell_counts = {"RBC": 0, "WBC": 0, "Platelets": 0}
|
103 |
+
|
104 |
+
# Count cells and check for WBC
|
105 |
+
has_wbc = False
|
106 |
+
# Display details of detected boxes
|
107 |
+
st.write("Detection Results:")
|
108 |
+
for box in results[0].boxes:
|
109 |
+
class_index = int(box.cls) # Get the class index
|
110 |
+
if class_index == 1: # RBC
|
111 |
+
cell_counts["RBC"] += 1
|
112 |
+
elif class_index == 2: # WBC
|
113 |
+
cell_counts["WBC"] += 1
|
114 |
+
has_wbc = True # WBC detected
|
115 |
+
elif class_index == 0: # Platelets
|
116 |
+
cell_counts["Platelets"] += 1
|
117 |
+
|
118 |
+
# Display bounding box information
|
119 |
+
#st.write(f"Bounding box: {box.xyxy}")
|
120 |
+
#st.write(f"Confidence: {box.conf}")
|
121 |
+
#st.write(f"Class: {box.cls}")
|
122 |
+
|
123 |
+
# Display the counts of each cell type
|
124 |
+
st.write("Cell Type Counts:")
|
125 |
+
st.write(pd.DataFrame.from_dict(cell_counts, orient='index', columns=['Count']))
|
126 |
+
# If a WBC is detected, run the second model
|
127 |
+
if has_wbc:
|
128 |
+
# Perform inference with the FastAI model
|
129 |
+
pred, idx, probs = fastai_model.predict(image)
|
130 |
+
st.write("White Blood Cell Classification:")
|
131 |
+
categories = ('EOSINOPHIL', 'LYMPHOCYTE', 'MONOCYTE', 'NEUTROPHIL')
|
132 |
+
results_dict = dict(zip(categories, map(float, probs)))
|
133 |
+
st.write(results_dict)
|
134 |
+
else:
|
135 |
+
st.write("Upload an image to start detection.")
|