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| import streamlit as st | |
| import numpy as np | |
| from PIL import Image | |
| from ultralytics import YOLO | |
| import time | |
| # Set page title and layout | |
| st.set_page_config(page_title="Blood Component Detection", page_icon="🩸", layout="wide") | |
| # Styling the app | |
| st.markdown(""" | |
| <style> | |
| .stApp { | |
| background-color: #f4f6f9; | |
| } | |
| .title { | |
| color: #000; | |
| font-size: 40px; | |
| font-weight: bold; | |
| margin-top: 20px; | |
| text-align: center; | |
| } | |
| .subtitle { | |
| color: #555; | |
| font-size: 20px; | |
| text-align: center; | |
| } | |
| .upload-container { | |
| background-color: #fff; | |
| padding: 20px; | |
| border-radius: 10px; | |
| box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1); | |
| margin-bottom: 20px; | |
| } | |
| .predict-btn { | |
| background-color: #4CAF50; | |
| color: white; | |
| padding: 10px 20px; | |
| border-radius: 5px; | |
| font-size: 18px; | |
| width: 100%; | |
| cursor: pointer; | |
| } | |
| .predict-btn:hover { | |
| background-color: #45a049; | |
| } | |
| .stImage { | |
| border-radius: 15px; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Set title and subtitle | |
| st.markdown('<div class="title">Blood Component Detection</div>', unsafe_allow_html=True) | |
| st.markdown('<div class="subtitle">Detect and classify blood cells in uploaded images</div>', unsafe_allow_html=True) | |
| # Load the YOLO model | |
| model = YOLO('blood_detection_model.pt') | |
| # Image uploader with custom style | |
| with st.container(): | |
| st.markdown('<div class="upload-container">', unsafe_allow_html=True) | |
| uploaded_image = st.file_uploader("Upload Image (JPG, JPEG, PNG)", type=['jpg', 'jpeg', 'png']) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| if uploaded_image is not None: | |
| # Display the uploaded image | |
| image = Image.open(uploaded_image) | |
| image = image.convert("RGB") | |
| st.image(image, caption="Uploaded Image", use_container_width=True) | |
| # Prediction button | |
| with st.container(): | |
| if st.button('Classify Image', key="predict", help="Click to start classification", use_container_width=True): | |
| st.write("Classifying... Please wait.") | |
| # Display a progress bar while the model processes | |
| progress_bar = st.progress(0) | |
| for i in range(100): | |
| time.sleep(0.01) | |
| progress_bar.progress(i+1) | |
| # Convert the image for prediction | |
| ### img_array = np.array(image) | |
| # Perform prediction with a low confidence threshold | |
| results = model.predict(source=image, imgsz=640, conf=0.1) | |
| # Display results | |
| for result in results: | |
| print(result.boxes.xyxy, result.boxes.cls, result.boxes.conf) | |
| # Print bounding boxes, class IDs, and confidence | |
| # Show results | |
| for i in range (1, 25): | |
| if len(result[0].boxes) > 0: | |
| # Extract detection results | |
| st.write("Bounding Boxes:", result[0].boxes.xyxy) | |
| st.write("Class IDs:", result[0].boxes.cls) | |
| st.write("Confidence Scores:", result[0].boxes.conf) | |
| # Plot the results (image with bounding boxes) | |
| output_image = result[i].plot() # This should plot bounding boxes | |
| st.image(output_image, caption="Predicted Image with Bounding Boxes", use_container_width=True) | |
| else: | |
| st.write("No objects detected.") | |
| st.write("Raw Prediction Results:", results) | |
| st.write("Boxes:", results[0].boxes.xyxy) | |
| st.write("Classes:", results[0].boxes.cls) | |
| st.write("Confidences:", results[0].boxes.conf) | |