Update app.py

app.py

@@ -1,188 +1,189 @@
-import streamlit as st
-import cv2
-import numpy as np
-from PIL import Image
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-from ultralytics import YOLO
-import time
-
- 
-st.set_page_config(
-    page_title="Blood Cell Detection App",
-    page_icon="🔬",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
- 
-st.markdown("""
-<style>
-    @import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;500;600&display=swap');
-    * { font-family: 'Poppins', sans-serif; }
-    .main-header { font-size: 2.5rem; font-weight: 600; color: #4B0082; text-align: center; margin-bottom: 1rem; }
-    .sub-header { font-size: 1.5rem; font-weight: 500; color: #6200EA; margin: 1rem 0; }
-    .card { background: white; border-radius: 0.8rem; padding: 1rem; margin-bottom: 1rem; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }
-    .stButton>button { background: #6200EA; color: white; border: none; border-radius: 0.5rem; padding: 0.5rem 1rem; }
-    .metric-card { text-align: center; padding: 1rem; }
-    .metric-value { font-size: 2rem; font-weight: 600; color: #6200EA; }
-    .metric-label { font-size: 0.9rem; color: #555; }
-    .cell-badge { padding: 0.2rem 0.5rem; border-radius: 1rem; font-size: 0.8rem; }
-    .rbc-badge { background: rgba(255,82,82,0.2); color: #FF5252; border: 1px solid #FF5252; }
-    .wbc-badge { background: rgba(33,150,243,0.2); color: #2196F3; border: 1px solid #2196F3; }
-    .plt-badge { background: rgba(76,175,80,0.2); color: #4CAF50; border: 1px solid #4CAF50; }
-    .footer { text-align: center; padding: 1rem; color: #777; font-size: 0.8rem; border-top: 1px solid #eee; margin-top: 2rem; }
-</style>
-""", unsafe_allow_html=True)
-
- 
-st.markdown("<h1 class='main-header'>Blood Cell Detection System</h1>", unsafe_allow_html=True)
-
- 
-tab1, tab2, tab3 = st.tabs(["📊 Analysis", "ℹ️ About", "❓ Help"])
-
- 
-with tab1:
-    
-    @st.cache_data
-    def preprocess_image(uploaded_file):
-        try:
-            bytes_data = uploaded_file.getvalue()
-            img = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
-            if img is None:
-                raise ValueError("Invalid image file")
-            return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        except Exception as e:
-            st.error(f"Error preprocessing image: {str(e)}")
-            return None
-
-    @st.cache_resource
-    def load_model():
-        try:
-            model = YOLO("bccd_yolov10_best.pt")   
-            return model
-        except Exception as e:
-            st.error(f"Error loading model: {str(e)}")
-            return None
-
-    def perform_inference(model, image):
-        try:
-            results = model.predict(image, conf=0.25)
-            return results[0]
-        except Exception as e:
-            st.error(f"Error during inference: {str(e)}")
-            return None
-
-    def create_visualization(image, results):
-        if not results.boxes:
-            return None
-        fig = go.Figure()
-        fig.add_trace(go.Image(z=image))
-        boxes = results.boxes.xyxy.cpu().numpy()
-        classes = results.boxes.cls.cpu().numpy()
-        confs = results.boxes.conf.cpu().numpy()
-        class_names = ["RBC", "WBC", "Platelets"]
-        colors = ["#FF5252", "#2196F3", "#4CAF50"]
-        for box, cls, conf in zip(boxes, classes, confs):
-            x0, y0, x1, y1 = box
-            cls_name = class_names[int(cls)]
-            color = colors[int(cls)]
-            fig.add_shape(type="rect", x0=x0, y0=y0, x1=x1, y1=y1, line=dict(color=color, width=2))
-            fig.add_annotation(x=x0, y=y0-5, text=f"{cls_name} ({conf:.2f})", showarrow=False, 
-                               font=dict(color="white", size=10), bgcolor=color, bordercolor=color)
-        fig.update_layout(width=700, height=500, margin=dict(l=0, r=0, b=0, t=0), showlegend=False)
-        fig.update_xaxes(showticklabels=False, showgrid=False)
-        fig.update_yaxes(showticklabels=False, showgrid=False)
-        return fig
-
-    def create_cell_counts_chart(results):
-        if not results.boxes:
-            return None
-        classes = results.boxes.cls.cpu().numpy()
-        class_names = ["RBC", "WBC", "Platelets"]
-        counts = {name: sum(1 for cls in classes if class_names[int(cls)] == name) for name in class_names}
-        df = pd.DataFrame({"Cell Type": list(counts.keys()), "Count": list(counts.values())})
-        fig = px.bar(df, x="Cell Type", y="Count", color="Cell Type", 
-                     color_discrete_sequence=["#FF5252", "#2196F3", "#4CAF50"], 
-                     text=df["Count"], height=400)
-        fig.update_layout(title="Cell Count Distribution", xaxis_title="", yaxis_title="Count")
-        fig.update_traces(textposition="outside")
-        return fig
-
-    # Main Content
-    st.markdown("<div class='card'>", unsafe_allow_html=True)
-    model = load_model()
-    if not model:
-        st.stop()
-
-    st.markdown("<h2 class='sub-header'>Upload Image</h2>", unsafe_allow_html=True)
-    uploaded_file = st.file_uploader("Choose an image (JPG, JPEG, PNG)", type=["jpg", "jpeg", "png"])
-    st.markdown("</div>", unsafe_allow_html=True)
-
-    if uploaded_file:
-        st.markdown("<div class='card'>", unsafe_allow_html=True)
-        col1, col2 = st.columns(2)
-        with col1:
-            image = preprocess_image(uploaded_file)
-            if image is None:
-                st.stop()
-            st.image(image, caption="Uploaded Image", use_container_width=True)
-        with col2:
-            if st.button("🔍 Detect Blood Cells"):
-                with st.spinner("Analyzing..."):
-                    time.sleep(1)  # Simulate processing
-                    results = perform_inference(model, image)
-                    if results and results.boxes:
-                        vis_fig = create_visualization(image, results)
-                        st.plotly_chart(vis_fig, use_container_width=True)
-                        
-                        # Metrics
-                        classes = results.boxes.cls.cpu().numpy()
-                        class_names = ["RBC", "WBC", "Platelets"]
-                        counts = {name: sum(1 for cls in classes if class_names[int(cls)] == name) for name in class_names}
-                        cols = st.columns(4)
-                        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)
-                        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)
-                        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)
-                        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)
-                        
-                     
-                        count_fig = create_cell_counts_chart(results)
-                        st.plotly_chart(count_fig, use_container_width=True)
-                        
-                        
-                        st.markdown("<h2 class='sub-header'>Detailed Results</h2>", unsafe_allow_html=True)
-                        data = [{"ID": i+1, "Cell Type": f"<span class='cell-badge {['rbc','wbc','plt'][int(cls)]}-badge'>{class_names[int(cls)]}</span>", 
-                                 "Confidence": f"{conf*100:.2f}%"} 
-                                for i, (cls, conf) in enumerate(zip(classes, results.boxes.conf.cpu().numpy()))]
-                        st.write(pd.DataFrame(data).to_html(escape=False, index=False), unsafe_allow_html=True)
-                        
-                  
-                        st.download_button("📊 Export as CSV", 
-                                          pd.DataFrame({"Cell Type": [class_names[int(cls)] for cls in classes], 
-                                                        "Confidence": [f"{conf*100:.2f}%" for conf in results.boxes.conf.cpu().numpy()]}).to_csv(index=False), 
-                                          "results.csv", "text/csv")
-                    else:
-                        st.warning("No cells detected or analysis failed.")
-        st.markdown("</div>", unsafe_allow_html=True)
-
- 
-with tab2:
-    st.markdown("<div class='card'>", unsafe_allow_html=True)
-    st.markdown("<h2 class='sub-header'>About</h2>", unsafe_allow_html=True)
-    st.markdown("""
-    This app uses YOLOv10 to detect Red Blood Cells (RBC), White Blood Cells (WBC), and Platelets in blood smear images. 
-    Upload an image in the Analysis tab to see counts, visualizations, and detailed results.
-    """)
-    st.markdown("</div>", unsafe_allow_html=True)
- 
-with tab3:
-    st.markdown("<div class='card'>", unsafe_allow_html=True)
-    st.markdown("<h2 class='sub-header'>Help</h2>", unsafe_allow_html=True)
-    st.markdown("""
-    - **Upload**: Select a JPG, JPEG, or PNG image in the Analysis tab.
-    - **Detect**: Click "Detect Blood Cells" to analyze.
-    - **Results**: View counts and export data as CSV.
-    - **Support**: Email [email protected] for issues.
-    """)
-    st.markdown("</div>", unsafe_allow_html=True)
+!pip install -r requirements.txt
+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from ultralytics import YOLO
+import time
+
+ 
+st.set_page_config(
+    page_title="Blood Cell Detection App",
+    page_icon="🔬",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+ 
+st.markdown("""
+<style>
+    @import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;500;600&display=swap');
+    * { font-family: 'Poppins', sans-serif; }
+    .main-header { font-size: 2.5rem; font-weight: 600; color: #4B0082; text-align: center; margin-bottom: 1rem; }
+    .sub-header { font-size: 1.5rem; font-weight: 500; color: #6200EA; margin: 1rem 0; }
+    .card { background: white; border-radius: 0.8rem; padding: 1rem; margin-bottom: 1rem; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }
+    .stButton>button { background: #6200EA; color: white; border: none; border-radius: 0.5rem; padding: 0.5rem 1rem; }
+    .metric-card { text-align: center; padding: 1rem; }
+    .metric-value { font-size: 2rem; font-weight: 600; color: #6200EA; }
+    .metric-label { font-size: 0.9rem; color: #555; }
+    .cell-badge { padding: 0.2rem 0.5rem; border-radius: 1rem; font-size: 0.8rem; }
+    .rbc-badge { background: rgba(255,82,82,0.2); color: #FF5252; border: 1px solid #FF5252; }
+    .wbc-badge { background: rgba(33,150,243,0.2); color: #2196F3; border: 1px solid #2196F3; }
+    .plt-badge { background: rgba(76,175,80,0.2); color: #4CAF50; border: 1px solid #4CAF50; }
+    .footer { text-align: center; padding: 1rem; color: #777; font-size: 0.8rem; border-top: 1px solid #eee; margin-top: 2rem; }
+</style>
+""", unsafe_allow_html=True)
+
+ 
+st.markdown("<h1 class='main-header'>Blood Cell Detection System</h1>", unsafe_allow_html=True)
+
+ 
+tab1, tab2, tab3 = st.tabs(["📊 Analysis", "ℹ️ About", "❓ Help"])
+
+ 
+with tab1:
+    
+    @st.cache_data
+    def preprocess_image(uploaded_file):
+        try:
+            bytes_data = uploaded_file.getvalue()
+            img = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
+            if img is None:
+                raise ValueError("Invalid image file")
+            return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        except Exception as e:
+            st.error(f"Error preprocessing image: {str(e)}")
+            return None
+
+    @st.cache_resource
+    def load_model():
+        try:
+            model = YOLO("bccd_yolov10_best.pt")   
+            return model
+        except Exception as e:
+            st.error(f"Error loading model: {str(e)}")
+            return None
+
+    def perform_inference(model, image):
+        try:
+            results = model.predict(image, conf=0.25)
+            return results[0]
+        except Exception as e:
+            st.error(f"Error during inference: {str(e)}")
+            return None
+
+    def create_visualization(image, results):
+        if not results.boxes:
+            return None
+        fig = go.Figure()
+        fig.add_trace(go.Image(z=image))
+        boxes = results.boxes.xyxy.cpu().numpy()
+        classes = results.boxes.cls.cpu().numpy()
+        confs = results.boxes.conf.cpu().numpy()
+        class_names = ["RBC", "WBC", "Platelets"]
+        colors = ["#FF5252", "#2196F3", "#4CAF50"]
+        for box, cls, conf in zip(boxes, classes, confs):
+            x0, y0, x1, y1 = box
+            cls_name = class_names[int(cls)]
+            color = colors[int(cls)]
+            fig.add_shape(type="rect", x0=x0, y0=y0, x1=x1, y1=y1, line=dict(color=color, width=2))
+            fig.add_annotation(x=x0, y=y0-5, text=f"{cls_name} ({conf:.2f})", showarrow=False, 
+                               font=dict(color="white", size=10), bgcolor=color, bordercolor=color)
+        fig.update_layout(width=700, height=500, margin=dict(l=0, r=0, b=0, t=0), showlegend=False)
+        fig.update_xaxes(showticklabels=False, showgrid=False)
+        fig.update_yaxes(showticklabels=False, showgrid=False)
+        return fig
+
+    def create_cell_counts_chart(results):
+        if not results.boxes:
+            return None
+        classes = results.boxes.cls.cpu().numpy()
+        class_names = ["RBC", "WBC", "Platelets"]
+        counts = {name: sum(1 for cls in classes if class_names[int(cls)] == name) for name in class_names}
+        df = pd.DataFrame({"Cell Type": list(counts.keys()), "Count": list(counts.values())})
+        fig = px.bar(df, x="Cell Type", y="Count", color="Cell Type", 
+                     color_discrete_sequence=["#FF5252", "#2196F3", "#4CAF50"], 
+                     text=df["Count"], height=400)
+        fig.update_layout(title="Cell Count Distribution", xaxis_title="", yaxis_title="Count")
+        fig.update_traces(textposition="outside")
+        return fig
+
+    # Main Content
+    st.markdown("<div class='card'>", unsafe_allow_html=True)
+    model = load_model()
+    if not model:
+        st.stop()
+
+    st.markdown("<h2 class='sub-header'>Upload Image</h2>", unsafe_allow_html=True)
+    uploaded_file = st.file_uploader("Choose an image (JPG, JPEG, PNG)", type=["jpg", "jpeg", "png"])
+    st.markdown("</div>", unsafe_allow_html=True)
+
+    if uploaded_file:
+        st.markdown("<div class='card'>", unsafe_allow_html=True)
+        col1, col2 = st.columns(2)
+        with col1:
+            image = preprocess_image(uploaded_file)
+            if image is None:
+                st.stop()
+            st.image(image, caption="Uploaded Image", use_container_width=True)
+        with col2:
+            if st.button("🔍 Detect Blood Cells"):
+                with st.spinner("Analyzing..."):
+                    time.sleep(1)  # Simulate processing
+                    results = perform_inference(model, image)
+                    if results and results.boxes:
+                        vis_fig = create_visualization(image, results)
+                        st.plotly_chart(vis_fig, use_container_width=True)
+                        
+                        # Metrics
+                        classes = results.boxes.cls.cpu().numpy()
+                        class_names = ["RBC", "WBC", "Platelets"]
+                        counts = {name: sum(1 for cls in classes if class_names[int(cls)] == name) for name in class_names}
+                        cols = st.columns(4)
+                        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)
+                        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)
+                        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)
+                        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)
+                        
+                     
+                        count_fig = create_cell_counts_chart(results)
+                        st.plotly_chart(count_fig, use_container_width=True)
+                        
+                        
+                        st.markdown("<h2 class='sub-header'>Detailed Results</h2>", unsafe_allow_html=True)
+                        data = [{"ID": i+1, "Cell Type": f"<span class='cell-badge {['rbc','wbc','plt'][int(cls)]}-badge'>{class_names[int(cls)]}</span>", 
+                                 "Confidence": f"{conf*100:.2f}%"} 
+                                for i, (cls, conf) in enumerate(zip(classes, results.boxes.conf.cpu().numpy()))]
+                        st.write(pd.DataFrame(data).to_html(escape=False, index=False), unsafe_allow_html=True)
+                        
+                  
+                        st.download_button("📊 Export as CSV", 
+                                          pd.DataFrame({"Cell Type": [class_names[int(cls)] for cls in classes], 
+                                                        "Confidence": [f"{conf*100:.2f}%" for conf in results.boxes.conf.cpu().numpy()]}).to_csv(index=False), 
+                                          "results.csv", "text/csv")
+                    else:
+                        st.warning("No cells detected or analysis failed.")
+        st.markdown("</div>", unsafe_allow_html=True)
+
+ 
+with tab2:
+    st.markdown("<div class='card'>", unsafe_allow_html=True)
+    st.markdown("<h2 class='sub-header'>About</h2>", unsafe_allow_html=True)
+    st.markdown("""
+    This app uses YOLOv10 to detect Red Blood Cells (RBC), White Blood Cells (WBC), and Platelets in blood smear images. 
+    Upload an image in the Analysis tab to see counts, visualizations, and detailed results.
+    """)
+    st.markdown("</div>", unsafe_allow_html=True)
+ 
+with tab3:
+    st.markdown("<div class='card'>", unsafe_allow_html=True)
+    st.markdown("<h2 class='sub-header'>Help</h2>", unsafe_allow_html=True)
+    st.markdown("""
+    - **Upload**: Select a JPG, JPEG, or PNG image in the Analysis tab.
+    - **Detect**: Click "Detect Blood Cells" to analyze.
+    - **Results**: View counts and export data as CSV.
+    - **Support**: Email [email protected] for issues.
+    """)
+    st.markdown("</div>", unsafe_allow_html=True)