Update app.py

app.py

@@ -6,55 +6,130 @@ import requests
 from io import BytesIO
 from fastai.vision.all import load_learner  
 
-# Initialize Streamlit app
-st.title("White Blood Cell Classifier")
 
-# Add a description or subtitle
-st.markdown("""
-This app allows you to classify white blood cells from an uploaded image. 
-You can upload an image of a blood sample, and the app will predict the type of white blood cell present. 
-Choose from various cell types like eosinophil, lymphocyte, monocyte, and neutrophil.
-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.
-""")
-
-# 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 classification", type=["jpg", "png"])
-
-if uploaded_file:
-    # Open the uploaded image
-    image = Image.open(uploaded_file).convert('RGB')
+options = st.selectbox("which model you wanna choose?", ("wbc classifier", "Blood Cell Detection with YOLOv8"))
 
+if options == "wbc classifier":
+    # Initialize Streamlit app
+    st.title("White Blood Cell Classifier")
     
-    # Display the uploaded image with a caption
-    st.image(image, caption="Reduced Size Image", use_column_width=False, width=150)  # 150 pixels wide
-
-    # Perform inference with the FastAI model
-    pred, idx, probs = fastai_model.predict(image)
-
-    # Display a title for the results section
-    st.subheader("White Blood Cell Classification Results")
-
-    # Define categories for classification
-    categories = ('EOSINOPHIL', 'LYMPHOCYTE', 'MONOCYTE', 'NEUTROPHIL')
-
-    # Create a DataFrame with classification probabilities
-    results_df = pd.DataFrame(
-        {'Cell Type': categories, 'Probability': probs.tolist()}
-    )
-     # Highlight the most likely class
-    most_likely_class = categories[idx]
-    st.success(f"Predicted Class: {most_likely_class}")
-
-    # Additional information about the probabilities
-    st.write("Detailed Classification Results:")
-    st.table(results_df)
-
-    # Display the probabilities as a bar chart
-    st.bar_chart(results_df.set_index('Cell Type'))
+    # Add a description or subtitle
+    st.markdown("""
+    This app allows you to classify white blood cells from an uploaded image. 
+    You can upload an image of a blood sample, and the app will predict the type of white blood cell present. 
+    Choose from various cell types like eosinophil, lymphocyte, monocyte, and neutrophil.
+    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.
+    """)
+    
+    # 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 classification", type=["jpg", "png"])
+    
+    if uploaded_file:
+        # Open the uploaded image
+        image = Image.open(uploaded_file).convert('RGB')
+    
+        
+        # Display the uploaded image with a caption
+        st.image(image, caption="Reduced Size Image", use_column_width=False, width=150)  # 150 pixels wide
+    
+        # Perform inference with the FastAI model
+        pred, idx, probs = fastai_model.predict(image)
+    
+        # Display a title for the results section
+        st.subheader("White Blood Cell Classification Results")
+    
+        # Define categories for classification
+        categories = ('EOSINOPHIL', 'LYMPHOCYTE', 'MONOCYTE', 'NEUTROPHIL')
+    
+        # Create a DataFrame with classification probabilities
+        results_df = pd.DataFrame(
+            {'Cell Type': categories, 'Probability': probs.tolist()}
+        )
+         # Highlight the most likely class
+        most_likely_class = categories[idx]
+        st.success(f"Predicted Class: {most_likely_class}")
+    
+        # Additional information about the probabilities
+        st.write("Detailed Classification Results:")
+        st.table(results_df)
+    
+        # Display the probabilities as a bar chart
+        st.bar_chart(results_df.set_index('Cell Type'))
+    
+       
+    else:
+        st.warning("Upload an image to start classification.")
 
-   
-else:
-    st.warning("Upload an image to start classification.")
+if options == "Blood Cell Detection with YOLOv8":
+    # Initialize Streamlit app
+    st.title("Blood Cell Detection with YOLOv8")
+    
+    # Load YOLO model
+    model = YOLO('keremberke/yolov8m-blood-cell-detection')
+    
+    # Set model parameters
+    model.overrides['conf'] = 0.25  # NMS confidence threshold
+    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"])
+    
+    if uploaded_file:
+        # Open the uploaded image
+        image = Image.open(uploaded_file)
+        
+        # Perform inference
+        results = model.predict(np.array(image))
+        
+        # Display results
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        
+        # Render detection results
+        rendered_image = render_result(model=model, image=image, result=results[0])
+        
+        # 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}
+        
+        # Count cells and check for WBC
+        has_wbc = False
+        # Display details of detected boxes
+        st.write("Detection Results:")
+        for box in results[0].boxes:
+            class_index = int(box.cls)  # Get the class index
+            if class_index == 1:  # RBC
+                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
+            
+            # 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']))
+        # 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.")