match streamlit

app.py

@@ -67,7 +67,7 @@ def classify_image(image):
     result_html = f"""
     <h2>Classification Result</h2>
     <p><strong>Predicted Class:</strong> {predicted_label}</p>
-    <p><strong>Date and Time:</strong> {probabilities}</p>
+    <p><strong>Probabilities(needs some work):</strong> {probabilities}</p>
     """
 
     # Return the result formatted in HTML
@@ -79,9 +79,10 @@ iface = gr.Interface(
     fn=classify_image,
     inputs=gr.Image(type="numpy", label="Upload an image"),
     outputs=gr.HTML(),  # Output formatted with HTML
-    title="Intracranial Hemorrhage Classifier",
-    description="""<h1>Intracranial Hemorrhage Classifier</h1>
-<p>This Gradio application allows classifying images related to intracranial hemorrhage using a Vision Transformer (ViT) model.</p>
+    title="",
+    description="""<h1>🧠 Intracranial Hemorrhage Detector</h1>
+    <p>Made with ❤️ by: Ines, Julia, Mo and Sep</p>
+
 
 <h2>Usage Instructions</h2>
 
@@ -89,10 +90,11 @@ iface = gr.Interface(
   <li>Click the "Choose File" button to upload an image related to intracranial hemorrhage.</li>
   <li>Wait a few seconds while the model processes the image.</li>
   <li>The result will show the predicted class related to intracranial hemorrhage.</li>
-  <li>The date and time of the classification will also be displayed.</li>
+  <li>The Probability of the classification will also be displayed.</li>
 </ol>
 
 <p>This model can identify classes of intracranial hemorrhage, such as "epidural", "intraparenchymal", "intraventricular", "normal", "subarachnoid", and "subdural".</p>
+<p>This Gradio application allows classifying images related to intracranial hemorrhage using a Vision Transformer (ViT) model.</p>
 
 <p>Please note that this is a demonstration model and the results may not be accurate for clinical purposes.</p>""",
 )