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import streamlit as st
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import cv2
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import numpy as np
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from PIL import Image
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import pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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from ultralytics import YOLO
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import time
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st.set_page_config(
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page_title="Blood Cell Detection App",
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page_icon="🔬",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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st.markdown("""
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<style>
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@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;500;600&display=swap');
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* { font-family: 'Poppins', sans-serif; }
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.main-header { font-size: 2.5rem; font-weight: 600; color: #4B0082; text-align: center; margin-bottom: 1rem; }
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.sub-header { font-size: 1.5rem; font-weight: 500; color: #6200EA; margin: 1rem 0; }
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.card { background: white; border-radius: 0.8rem; padding: 1rem; margin-bottom: 1rem; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }
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.stButton>button { background: #6200EA; color: white; border: none; border-radius: 0.5rem; padding: 0.5rem 1rem; }
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.metric-card { text-align: center; padding: 1rem; }
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.metric-value { font-size: 2rem; font-weight: 600; color: #6200EA; }
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.metric-label { font-size: 0.9rem; color: #555; }
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.cell-badge { padding: 0.2rem 0.5rem; border-radius: 1rem; font-size: 0.8rem; }
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.rbc-badge { background: rgba(255,82,82,0.2); color: #FF5252; border: 1px solid #FF5252; }
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.wbc-badge { background: rgba(33,150,243,0.2); color: #2196F3; border: 1px solid #2196F3; }
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.plt-badge { background: rgba(76,175,80,0.2); color: #4CAF50; border: 1px solid #4CAF50; }
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.footer { text-align: center; padding: 1rem; color: #777; font-size: 0.8rem; border-top: 1px solid #eee; margin-top: 2rem; }
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</style>
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""", unsafe_allow_html=True)
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st.markdown("<h1 class='main-header'>Blood Cell Detection System</h1>", unsafe_allow_html=True)
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tab1, tab2, tab3 = st.tabs(["📊 Analysis", "ℹ️ About", "❓ Help"])
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with tab1:
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@st.cache_data
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def preprocess_image(uploaded_file):
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try:
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bytes_data = uploaded_file.getvalue()
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img = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
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if img is None:
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raise ValueError("Invalid image file")
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return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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except Exception as e:
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st.error(f"Error preprocessing image: {str(e)}")
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return None
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@st.cache_resource
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def load_model():
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try:
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model = YOLO("bccd_yolov10_best.pt")
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return model
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except Exception as e:
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st.error(f"Error loading model: {str(e)}")
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return None
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def perform_inference(model, image):
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try:
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results = model.predict(image, conf=0.25)
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return results[0]
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except Exception as e:
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st.error(f"Error during inference: {str(e)}")
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return None
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def create_visualization(image, results):
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if not results.boxes:
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return None
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fig = go.Figure()
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fig.add_trace(go.Image(z=image))
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boxes = results.boxes.xyxy.cpu().numpy()
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classes = results.boxes.cls.cpu().numpy()
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confs = results.boxes.conf.cpu().numpy()
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class_names = ["RBC", "WBC", "Platelets"]
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colors = ["#FF5252", "#2196F3", "#4CAF50"]
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for box, cls, conf in zip(boxes, classes, confs):
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x0, y0, x1, y1 = box
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cls_name = class_names[int(cls)]
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color = colors[int(cls)]
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fig.add_shape(type="rect", x0=x0, y0=y0, x1=x1, y1=y1, line=dict(color=color, width=2))
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fig.add_annotation(x=x0, y=y0-5, text=f"{cls_name} ({conf:.2f})", showarrow=False,
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font=dict(color="white", size=10), bgcolor=color, bordercolor=color)
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fig.update_layout(width=700, height=500, margin=dict(l=0, r=0, b=0, t=0), showlegend=False)
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fig.update_xaxes(showticklabels=False, showgrid=False)
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fig.update_yaxes(showticklabels=False, showgrid=False)
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return fig
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def create_cell_counts_chart(results):
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if not results.boxes:
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return None
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classes = results.boxes.cls.cpu().numpy()
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class_names = ["RBC", "WBC", "Platelets"]
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counts = {name: sum(1 for cls in classes if class_names[int(cls)] == name) for name in class_names}
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df = pd.DataFrame({"Cell Type": list(counts.keys()), "Count": list(counts.values())})
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fig = px.bar(df, x="Cell Type", y="Count", color="Cell Type",
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color_discrete_sequence=["#FF5252", "#2196F3", "#4CAF50"],
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text=df["Count"], height=400)
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fig.update_layout(title="Cell Count Distribution", xaxis_title="", yaxis_title="Count")
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fig.update_traces(textposition="outside")
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return fig
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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model = load_model()
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if not model:
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st.stop()
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st.markdown("<h2 class='sub-header'>Upload Image</h2>", unsafe_allow_html=True)
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uploaded_file = st.file_uploader("Choose an image (JPG, JPEG, PNG)", type=["jpg", "jpeg", "png"])
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st.markdown("</div>", unsafe_allow_html=True)
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if uploaded_file:
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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col1, col2 = st.columns(2)
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with col1:
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image = preprocess_image(uploaded_file)
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if image is None:
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st.stop()
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st.image(image, caption="Uploaded Image", use_container_width=True)
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with col2:
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if st.button("🔍 Detect Blood Cells"):
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with st.spinner("Analyzing..."):
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time.sleep(1)
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results = perform_inference(model, image)
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if results and results.boxes:
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vis_fig = create_visualization(image, results)
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st.plotly_chart(vis_fig, use_container_width=True)
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classes = results.boxes.cls.cpu().numpy()
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class_names = ["RBC", "WBC", "Platelets"]
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counts = {name: sum(1 for cls in classes if class_names[int(cls)] == name) for name in class_names}
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cols = st.columns(4)
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cols[0].markdown(f"<div class='metric-card'><div class='metric-value'>{len(classes)}</div><div class='metric-label'>Total Cells</div></div>", unsafe_allow_html=True)
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cols[1].markdown(f"<div class='metric-card'><div class='metric-value' style='color:#FF5252'>{counts['RBC']}</div><div class='metric-label'>RBC</div></div>", unsafe_allow_html=True)
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cols[2].markdown(f"<div class='metric-card'><div class='metric-value' style='color:#2196F3'>{counts['WBC']}</div><div class='metric-label'>WBC</div></div>", unsafe_allow_html=True)
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cols[3].markdown(f"<div class='metric-card'><div class='metric-value' style='color:#4CAF50'>{counts['Platelets']}</div><div class='metric-label'>Platelets</div></div>", unsafe_allow_html=True)
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count_fig = create_cell_counts_chart(results)
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st.plotly_chart(count_fig, use_container_width=True)
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st.markdown("<h2 class='sub-header'>Detailed Results</h2>", unsafe_allow_html=True)
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data = [{"ID": i+1, "Cell Type": f"<span class='cell-badge {['rbc','wbc','plt'][int(cls)]}-badge'>{class_names[int(cls)]}</span>",
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"Confidence": f"{conf*100:.2f}%"}
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for i, (cls, conf) in enumerate(zip(classes, results.boxes.conf.cpu().numpy()))]
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st.write(pd.DataFrame(data).to_html(escape=False, index=False), unsafe_allow_html=True)
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st.download_button("📊 Export as CSV",
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pd.DataFrame({"Cell Type": [class_names[int(cls)] for cls in classes],
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"Confidence": [f"{conf*100:.2f}%" for conf in results.boxes.conf.cpu().numpy()]}).to_csv(index=False),
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"results.csv", "text/csv")
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else:
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st.warning("No cells detected or analysis failed.")
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st.markdown("</div>", unsafe_allow_html=True)
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with tab2:
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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st.markdown("<h2 class='sub-header'>About</h2>", unsafe_allow_html=True)
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st.markdown("""
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This app uses YOLOv10 to detect Red Blood Cells (RBC), White Blood Cells (WBC), and Platelets in blood smear images.
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Upload an image in the Analysis tab to see counts, visualizations, and detailed results.
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""")
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st.markdown("</div>", unsafe_allow_html=True)
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with tab3:
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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st.markdown("<h2 class='sub-header'>Help</h2>", unsafe_allow_html=True)
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st.markdown("""
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- **Upload**: Select a JPG, JPEG, or PNG image in the Analysis tab.
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- **Detect**: Click "Detect Blood Cells" to analyze.
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- **Results**: View counts and export data as CSV.
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- **Support**: Email [email protected] for issues.
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""")
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st.markdown("</div>", unsafe_allow_html=True)
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