Upload app.py

app.py

@@ -35,7 +35,7 @@ def predict(image_input):
     return f"Predicted Class: {predicted_class} (Confidence: {prediction:.5f})"
 
 # Hugging Face Model Repository ID
-REPO_ID = "skngew/9053220B"  # Change this to your actual repo ID
+REPO_ID = "skngew/9053220B"  # my actual repo ID
 
 # Load the model
 model = load_model(REPO_ID)
@@ -45,6 +45,7 @@ student_id = "Student ID: 9053220B"
 
 # Markdown description to show classification threshold
 threshold_info = """
+### EfficientNetB0 (Feature Extraction)
 ### Classification Threshold:
 - A tyre is classified as **Good** if the confidence score is **≥ 0.5**.
 - A tyre is classified as **Defective** if the confidence score is **< 0.5**.
@@ -58,7 +59,7 @@ interface = gr.Interface(
     title="Binary Classification: Good vs. Defective Tire",
     description=student_id,
     allow_flagging="never",
-    examples=[],  # You can add example images here if needed
+    examples=[],  #Can add examples here
 )
 
 # Add the threshold information markdown