Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 from ultralyticsplus import YOLO, render_result
 import PIL.Image as Image
 import numpy as np
+import pandas as pd
 import requests
 from io import BytesIO
 
@@ -35,18 +36,11 @@ if uploaded_file:
     
     # Show the rendered result
     st.image(rendered_image, caption="Detection Results", use_column_width=True)
+    
     # Count the number of each cell type
     cell_counts = {"RBC": 0, "WBC": 0, "Platelets": 0}
 
-
-    
-
-else:
-    for box in results[0].boxes:
-        st.write(f"Bounding box: {box.xyxy}")
-        st.write(f"Confidence: {box.conf}")
-        st.write(f"Class: {box.cls}")
-     # Display details of detected boxes
+    # Display details of detected boxes
     st.write("Detection Results:")
     for box in results[0].boxes:
         class_index = int(box.cls)  # Get the class index
@@ -56,9 +50,15 @@ else:
             cell_counts["WBC"] += 1
         elif class_index == 0:  # Platelets
             cell_counts["Platelets"] += 1
+        
+        # Display bounding box information
+        st.write(f"Bounding box: {box.xyxy}")
+        st.write(f"Confidence: {box.conf}")
+        st.write(f"Class: {box.cls}")
 
     # Display the counts of each cell type
     st.write("Cell Type Counts:")
     st.write(pd.DataFrame.from_dict(cell_counts, orient='index', columns=['Count']))
+
 else:
-    st.write("Upload an image to start detection")
+    st.write("Upload an image to start detection.")