login

app.py

@@ -5,7 +5,6 @@ from sahi.predict import get_sliced_prediction
 from sahi import AutoDetectionModel
 from PIL import Image
 import plotly.graph_objects as go
-import spaces
 import torch
 
 
@@ -102,7 +101,7 @@ class Detection:
             if category_name in category_areas:
                 category_areas[category_name].append(area)
 
-        # Create individual area distribution histograms for each category
+        # Create individual area distribution histograms for each ctegory
         individual_graphs = {}
         for category in ['Nicks', 'Dents', 'Scratches', 'Pittings']:
             areas = category_areas[category]
@@ -209,7 +208,6 @@ def upload_image(image):
     return image
 
 
-@spaces.GPU
 def apply_detection(image):
     """Run object detection on the uploaded image and return the annotated image.""" 
     # Convert image from PIL to NumPy array
@@ -252,75 +250,7 @@ body{
     background-color: #fff;
 }
 
-nav{
-    padding: 0 80px;
-    display: flex;
-    align-items: center;
-    justify-content: space-between;
-}
-
-
-.nav-logo{
-    margin-top: 20px;
-}
 
-.astarlogo{
-    width: 230px;
-    display: flex;
-    border-style: none;
-    display: none;
-}
-
-
-.nav-links{
-    list-style: none;
-    display: flex;
-    align-items: center;
-    gap: 3rem ;
-}
-
-.link a{
-    position: relative;
-    padding-bottom: 0.75rem;
-    color:#083484;
-    font-size: 1rem;
-    font-weight: 600;
-    font-family: 'Poppins',sans-serif;
-}
-
-.link a::after {
-    content: "";
-    position: absolute;
-    height: 2px;
-    width: 0;
-    bottom: 0;
-    left: 0;
-    background-color: #083484;
-    transition: all 0.3s ease;
-
-}
-
-.link a:hover::after{
-    width: 70%;
-}
-
-nav .login button{
-    padding: 8px 14px;
-    border: none;
-    cursor: pointer;
-    background-color: transparent;
-}
-
-nav .login button#signup{
-    background-color: #083484;
-    color: #fff;
-    border-radius: 4px;
-    margin-right: 14px;
-    padding: 15px 20px;
-    margin-top: 25px;
-    display: none;
-
-}
 
 header{
     padding: 0 80px;
@@ -424,22 +354,6 @@ function refresh() {
 with gr.Blocks(css = css,js=js_func) as demo:
 
     gr.HTML("""             
-    <nav>
-        <div class = "nav-logo" >
-            <a href="#">
-                <img class="astarlogo" src="" >
-            </a>
-        </div>
-        <ul class="nav-links">
-            <li class = "link"><a href="#">HOME</a></li>
-            <li id = "link1" class = "link"><a href="#">OFFLINE DETECTION</a></li>
-            <li id = "link2" class = "link"><a href="#">CONTACT US</a></li>
-        </ul>
-        <div class="login">
-            <button id="signup">Get Started</button>
-        </div>
-    </nav>
-    
 
     <header>
         <div class="left">
@@ -474,12 +388,10 @@ with gr.Blocks(css = css,js=js_func) as demo:
         with gr.Column():
             gr.Markdown("### Output")
             output_image_component = gr.Image(type="pil", label="Annotated Image")
+            apply_detection_btn = gr.Button("Apply Detection")
+            output_annotations = gr.State()  # Store annotations
+            apply_detection_btn.click(apply_detection, inputs=upload_image_component, outputs=[output_image_component, output_annotations])
 
-    # Button for Object Detection below the columns
-    with gr.Row():  # Create a new row for the button
-        apply_detection_btn = gr.Button("Apply Detection")
-        output_annotations = gr.State()  # Store annotations
-        apply_detection_btn.click(apply_detection, inputs=upload_image_component, outputs=[output_image_component, output_annotations])
 
     # Row for the graphs
     with gr.Row():
@@ -491,7 +403,7 @@ with gr.Blocks(css = css,js=js_func) as demo:
 
     # Button to generate graphs
     with gr.Row():
-        graph_btn = gr.Button("Generate Graphs")
+        graph_btn = gr.Button("Generate Area Distribution Graphs")
         graph_btn.click(generate_graphs_btn, inputs=output_annotations, outputs=[
             nicks_graph_component, dents_graph_component, 
             scratches_graph_component, pittings_graph_component
@@ -501,14 +413,14 @@ with gr.Blocks(css = css,js=js_func) as demo:
     with gr.Row():
         frequency_graph_component = gr.Plot(label="Defect Frequency Distribution")  # Frequency Graph
 
-    # Additional row for frequency graph button (if needed)
+    # Row for frequency graph btn
     with gr.Row():
-        freq_graph_btn = gr.Button("Refresh Frequency Graph")
+        freq_graph_btn = gr.Button("Generate Frequency Graph")
         freq_graph_btn.click(detection.generate_frequency_graph, 
                               inputs=output_annotations, 
                               outputs=frequency_graph_component)
 
 # Launch the Gradio interface
-demo.launch(share=True)
+demo.launch(auth=("user", "ois"),auth_message= "Enter username and password",share=True)