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| import streamlit as st | |
| import PIL.Image as Image | |
| import numpy as np | |
| import pandas as pd | |
| import requests | |
| from io import BytesIO | |
| from fastai.vision.all import load_learner | |
| 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") | |
| # 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.") | |
| 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.") | |