Update app.py

app.py

@@ -1,5 +1,6 @@
 from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
 import gradio as gr
+import numpy as np
 from PIL import Image
 
 # Load the model and feature extractor
@@ -7,13 +8,33 @@ model = SegformerForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b
 feature_extractor = SegformerFeatureExtractor.from_pretrained("mattmdjaga/segformer_b2_clothes")
 
 def predict(image):
+    # Prepare the image for the model
     inputs = feature_extractor(images=image, return_tensors="pt")
+    # Get model outputs
     outputs = model(**inputs)
-    # Decode outputs and return results as needed
-    return "Segmentation output placeholder"  # Replace with actual processing
+    
+    # Get the segmentation logits
+    logits = outputs.logits
+    # Apply softmax to get probabilities
+    probabilities = logits.softmax(dim=1)  # shape: (batch_size, num_classes, height, width)
+    
+    # Get the predicted class for each pixel
+    predicted_class = probabilities.argmax(dim=1).squeeze().cpu().numpy()  # shape: (height, width)
+    
+    # Create a color map (you can define your own color mapping for different classes)
+    color_map = np.array([[0, 0, 0],    # Class 0 - background
+                          [255, 0, 0],  # Class 1 - red
+                          [0, 255, 0],  # Class 2 - green
+                          [0, 0, 255]]) # Class 3 - blue
+
+    # Create an output mask image
+    mask_image = color_map[predicted_class]  # Map class indices to colors
+    mask_image = Image.fromarray(mask_image.astype('uint8'))  # Convert to PIL Image
+    
+    return mask_image
 
 def segmentation_interface(image):
     return predict(image)
 
 # Create a Gradio interface for image segmentation
-gr.Interface(fn=segmentation_interface, inputs="image", outputs="text").launch()
+gr.Interface(fn=segmentation_interface, inputs="image", outputs="image").launch()