Create app.py

app.py

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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('/Users/abhinavyadav/Downloads/final_best(blood_detection).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)
+
+
+
+