Update app.py

app.py

@@ -2,13 +2,12 @@ import gradio as gr
 import torch
 from torchvision import transforms
 from PIL import Image
-import os
 
 # Define the lesion type mapping
 lesion_type_dict = {
     0: 'Actinic keratoses',
     1: 'Basal cell carcinoma',
-    2: 'Benign keratosis-like lesions',
+    2: 'Benign keratosis-like lesions ',
     3: 'Dermatofibroma',
     4: 'Melanocytic nevi',
     5: 'Melanoma',
@@ -24,6 +23,7 @@ model = load_model()  # Load the model onto CPU
 
 # Function to preprocess the image and predict the class
 def classify_img(img):
+    # Preprocess the image
     preprocess = transforms.Compose([
         transforms.Resize((224, 224)),  # Adjust as needed
         transforms.ToTensor(),
@@ -55,25 +55,25 @@ def classify_img(img):
 
     return output_text  # Return formatted text as a single string
 
+# Gradio interface setup
+image = gr.Image()  # Input is an image
+label = gr.Textbox()  # Output as formatted text
 
-# Nested list with each inner list containing image path and label
-example_images = [
-    ["examples/Actinic keratoses.jpg", "Actinic keratoses"],  # First image and its label
-    ["examples/Basal cell carcinoma.jpg", "Basal cell carcinoma"],  # Second image and its label
-    ["examples/Benign keratosis-like lesions.jpg", "Benign keratosis-like lesions"],  # And so on
-    ["examples/Dermatofibroma.jpg", "Dermatofibroma"],
-    ["examples/melanoma.jpg", "Melanoma"],
-    ["examples/Melanocytic nevi.jpg", "Melanocytic nevi"],
-    ["examples/Vascular lesions.jpg", "Vascular lesions"],
-]
-
-# Set up Gradio interface with examples as a list of tuples
+# Set up Gradio interface
 iface = gr.Interface(
     fn=classify_img,  # Prediction function
-    inputs=gr.Image(),  # Image input
-    outputs=gr.Textbox(),  # Output as formatted text
-    examples=example_images,  # List of tuples containing image paths and labels
+    inputs=image,  # Image input
+    outputs=label,  # Output as formatted text
+    examples=[
+        ["examples/Actinic keratoses.jpg", "Actinic keratoses"],  # First image and its label
+        ["examples/Basal cell carcinoma.jpg", "Basal cell carcinoma"],  # Second image and its label
+        ["examples/Benign keratosis-like lesions.jpg", "Benign keratosis-like lesions"],  # And so on
+        ["examples/Dermatofibroma.jpg", "Dermatofibroma"],
+        ["examples/melanoma.jpg", "Melanoma"],
+        ["examples/Melanocytic nevi.jpg", "Melanocytic nevi"],
+        ["examples/Vascular lesions.jpg", "Vascular lesions"],
+    ]
 )
 
 # Launch the Gradio interface
-iface.launch()  # Start the Gradio interface
+iface.launch()  # Start the local Gradio server