Update app.py

app.py

@@ -1,148 +1,58 @@
-import PIL
 import streamlit as st
-from ultralytics import YOLO
-
-# Give the path of the best.pt (best weights)
-model_path = 'best.pt'
-
-# Setting page layout
-st.set_page_config(
-    page_title="NISH(Neural Intelligence Synthesis Hub)",  # Setting page title
-    page_icon="NK logo.jpeg",     # Setting page icon
-    layout="wide",      # Setting layout to wide
-    initial_sidebar_state="expanded",    # Expanding sidebar by default
-    
-)
-
-# Creating sidebar
-with st.sidebar:
-    st.header("Image Config")     # Adding header to sidebar
-    # Adding file uploader to sidebar for selecting images
-    source_img = st.file_uploader(
-        "Upload an image...", type=("jpg", "jpeg", "png", 'bmp', 'webp'))
-
-    # Model Options
-    confidence = float(st.slider(
-        "Select Model Confidence", 25, 100, 40)) / 100
-
-# Creating main page heading
-st.title("Object Detection")
-st.caption('Updload a photo by selecting :blue[Browse files]')
-st.caption('Then click the :blue[Detect Objects] button and check the result.')
-# Creating two columns on the main page
-col1, col2 = st.columns(2)
-
-# Adding image to the first column if image is uploaded
-with col1:
-    if source_img:
-        # Opening the uploaded image
-        uploaded_image = PIL.Image.open(source_img)
-        image_width, image_height = uploaded_image.size
-        # Adding the uploaded image to the page with a caption
-        st.image(source_img,
-                 caption="Uploaded Image",
-                 width=image_width
-                 )
-
-try:
-    model = YOLO(model_path)
-except Exception as ex:
-    st.error(
-        f"Unable to load model. Check the specified path: {model_path}")
-    st.error(ex)
-
-if st.sidebar.button('Detect Objects'):
-    res = model.predict(uploaded_image,
-                        conf=confidence,
-                        line_width=1, 
-                        show_labels=False, 
-                        show_conf=False
-                        )
-    boxes = res[0].boxes
-    res_plotted = res[0].plot(labels=False, line_width=1)[:, :, ::-1]
-    with col2:
-        st.image(res_plotted,
-                 caption='Detected Image',
-                 width=image_width                 
-                 )
-        try:
-            st.write(f'Number of empty slots detected: {len(boxes)}')
-            with st.expander("Detection Results (xywh)"):                
-                for box in boxes:
-                    st.write(box.xywh)
-        except Exception as ex:
-            st.write("No image is uploaded yet!")
-import PIL
-import streamlit as st
-from ultralytics import YOLO
-
-# Give the path of the best.pt (best weights)
-model_path = 'best.pt'
-
-# Setting page layout
-st.set_page_config(
-    page_title="NISH(Neural Intelligence Synthesis Hub)",  # Setting page title
-    page_icon="NK logo.jpeg",     # Setting page icon
-    layout="wide",      # Setting layout to wide
-    initial_sidebar_state="expanded",    # Expanding sidebar by default
+import cv2
+import numpy as np
+import torch
+from torchvision import transforms
+
+# Load YOLOv5 models
+models = []
+models.append(torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True))
+models.append(torch.hub.load('ultralytics/yolov5', 'yolov5m', pretrained=True))
+models.append(torch.hub.load('ultralytics/yolov5', 'yolov5l', pretrained=True))
+models.append(torch.hub.load('ultralytics/yolov5', 'yolov5x', pretrained=True))
+
+# Custom CSS
+html_style = """
+<style>
+.container {
+    padding: 20px;
+    background-color: #f9f9f9;
+    border-radius: 10px;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+}
+.title {
+    color: #ff69b4;
+    font-size: 36px;
+    text-align: center;
+    margin-bottom: 30px;
+}
+.subheader {
+    color: #ff69b4;
+    font-size: 24px;
+    margin-top: 20px;
+}
+.image-container {
+    margin-top: 20px;
+    text-align: center;
+}
+</style>
+"""
+
+st.markdown(html_style, unsafe_allow_html=True)
+
+st.markdown("<h1 class='title'>AI Skin Analyzer</h1>", unsafe_allow_html=True)
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    st.markdown("<h2 class='subheader'>Model Predictions:</h2>", unsafe_allow_html=True)
     
-)
-
-# Creating sidebar
-with st.sidebar:
-    st.header("Image Config")     # Adding header to sidebar
-    # Adding file uploader to sidebar for selecting images
-    source_img = st.file_uploader(
-        "Upload an image...", type=("jpg", "jpeg", "png", 'bmp', 'webp'))
-
-    # Model Options
-    confidence = float(st.slider(
-        "Select Model Confidence", 25, 100, 40)) / 100
-
-# Creating main page heading
-st.title("Object Detection")
-st.caption('Updload a photo by selecting :blue[Browse files]')
-st.caption('Then click the :blue[Detect Objects] button and check the result.')
-# Creating two columns on the main page
-col1, col2 = st.columns(2)
-
-# Adding image to the first column if image is uploaded
-with col1:
-    if source_img:
-        # Opening the uploaded image
-        uploaded_image = PIL.Image.open(source_img)
-        image_width, image_height = uploaded_image.size
-        # Adding the uploaded image to the page with a caption
-        st.image(source_img,
-                 caption="Uploaded Image",
-                 width=image_width
-                 )
-
-try:
-    model = YOLO(model_path)
-except Exception as ex:
-    st.error(
-        f"Unable to load model. Check the specified path: {model_path}")
-    st.error(ex)
-
-if st.sidebar.button('Detect Objects'):
-    res = model.predict(uploaded_image,
-                        conf=confidence,
-                        line_width=1, 
-                        show_labels=False, 
-                        show_conf=False
-                        )
-    boxes = res[0].boxes
-    res_plotted = res[0].plot(labels=False, line_width=1)[:, :, ::-1]
-    with col2:
-        st.image(res_plotted,
-                 caption='Detected Image',
-                 width=image_width                 
-                 )
-        try:
-            st.write(f'Number of empty slots detected: {len(boxes)}')
-            with st.expander("Detection Results (xywh)"):                
-                for box in boxes:
-                    st.write(box.xywh)
-        except Exception as ex:
-            st.write("No image is uploaded yet!")
+    # Perform object detection for each model
+    for i, model in enumerate(models):
+        st.markdown(f"<h3 class='subheader'>Model {i+1}</h3>", unsafe_allow_html=True)
+        results = model(image)
+        results.render()
+        output_image = results.imgs[0]
+        st.image(output_image, use_column_width=True)