Update app.py

app.py

@@ -5,6 +5,7 @@ import numpy as np
 import pandas as pd
 import requests
 from io import BytesIO
+from fastai.vision.all import load_learner
 
 # Initialize Streamlit app
 st.title("Blood Cell Detection with YOLOv8")
@@ -18,6 +19,9 @@ model.overrides['iou'] = 0.45   # NMS IoU threshold
 model.overrides['agnostic_nms'] = False  # NMS class-agnostic
 model.overrides['max_det'] = 1000  # Maximum number of detections per image
 
+# Load the FastAI model for WBC identification
+fastai_model = load_learner('model1.pkl')
+
 # File uploader for image input
 uploaded_file = st.file_uploader("Upload an image for detection", type=["jpg", "png"])
 
@@ -39,7 +43,9 @@ if uploaded_file:
     
     # Count the number of each cell type
     cell_counts = {"RBC": 0, "WBC": 0, "Platelets": 0}
-
+    
+    # Count cells and check for WBC
+    has_wbc = False
     # Display details of detected boxes
     st.write("Detection Results:")
     for box in results[0].boxes:
@@ -48,6 +54,7 @@ if uploaded_file:
             cell_counts["RBC"] += 1
         elif class_index == 2:  # WBC
             cell_counts["WBC"] += 1
+            has_wbc = True  # WBC detected
         elif class_index == 0:  # Platelets
             cell_counts["Platelets"] += 1
         
@@ -59,6 +66,13 @@ if uploaded_file:
     # Display the counts of each cell type
     st.write("Cell Type Counts:")
     st.write(pd.DataFrame.from_dict(cell_counts, orient='index', columns=['Count']))
-
+    # If a WBC is detected, run the second model
+    if has_wbc:
+        # Perform inference with the FastAI model
+        pred, idx, probs = fastai_model.predict(image)
+        st.write("White Blood Cell Classification:")
+        categories = ('EOSINOPHIL', 'LYMPHOCYTE', 'MONOCYTE', 'NEUTROPHIL')
+        results_dict = dict(zip(categories, map(float, probs)))
+        st.write(results_dict)
 else:
     st.write("Upload an image to start detection.")