Update app.py

app.py

@@ -7,6 +7,7 @@ from PIL import Image
 import requests
 import base64
 from io import BytesIO
+import os
 
 # Define the number of classes
 num_classes = 2  # Update with the actual number of classes in your dataset
@@ -68,6 +69,15 @@ def predict(image):
                 print(f"Error fetching image from URL: {e}")
                 return f"Error fetching image from URL: {e}"
 
+        # Check if the input is a local file path
+        elif isinstance(image, str) and os.path.isfile(image):
+            try:
+                image = Image.open(image)
+                print(f"Loaded image from local path: {image}")
+            except Exception as e:
+                print(f"Error loading image from local path: {e}")
+                return f"Error loading image from local path: {e}"
+
         # Validate that the image is correctly loaded
         if not isinstance(image, Image.Image):
             print("Invalid image format received.")
@@ -97,7 +107,7 @@ def predict(image):
 # Create the Gradio interface
 iface = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="pil", label="Upload an image or provide a URL"),  # Input: Image or URL
+    inputs=gr.Image(type="pil", label="Upload an image or provide a URL or local path"),  # Input: Image, URL, or Local Path
     outputs=gr.Textbox(label="Prediction Result"),  # Output: Predicted class
     live=True,
     title="Maize Anomaly Detection",