Update app.py

app.py

@@ -1,66 +1,7 @@
-import torch
-from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation, DPTFeatureExtractor, DPTForDepthEstimation
-from PIL import Image, ImageFilter
-import numpy as np
 import gradio as gr
 
-# Load pre-trained models and feature extractors
-seg_feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
-seg_model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
-depth_feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
-depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
+def greet(name):
+    return "Hello" + name + "!!"
 
-def process_image(image):
-    # Preprocess the input image
-    image = image.resize((512, 512))
-    
-    # Perform semantic segmentation
-    seg_inputs = seg_feature_extractor(images=image, return_tensors="pt")
-    with torch.no_grad():
-        seg_outputs = seg_model(**seg_inputs)
-    seg_logits = seg_outputs.logits
-    segmentation = torch.argmax(seg_logits, dim=1)[0].numpy()
-    
-    # Create binary mask for 'person' class
-    person_class_index = 12
-    binary_mask = (segmentation == person_class_index).astype(np.uint8) * 255
-    
-    # Perform depth estimation
-    depth_inputs = depth_feature_extractor(images=image, return_tensors="pt")
-    with torch.no_grad():
-        depth_outputs = depth_model(**depth_inputs)
-    predicted_depth = depth_outputs.predicted_depth[0].cpu().numpy()
-    normalized_depth = (predicted_depth - predicted_depth.min()) / (predicted_depth.max() - predicted_depth.min())
-    inverted_depth = 1 - normalized_depth
-    depth_weight_resized = np.array(Image.fromarray((inverted_depth * 255).astype(np.uint8)).resize((512, 512))) / 255.0
-    depth_weight_resized = depth_weight_resized[:, :, np.newaxis]
-    
-    # Create blurred background effect
-    blurred_image = image.filter(ImageFilter.GaussianBlur(radius=15))
-    original_np = np.array(image).astype(np.float32)
-    blurred_np = np.array(blurred_image).astype(np.float32)
-    composite_np = (1 - depth_weight_resized) * original_np + depth_weight_resized * blurred_np
-    composite_image = Image.fromarray(np.clip(composite_np, 0, 255).astype(np.uint8))
-    
-    # Return results
-    binary_mask_image = Image.fromarray(binary_mask)
-    depth_map_image = Image.fromarray((normalized_depth * 255).astype(np.uint8))
-    return image, binary_mask_image, depth_map_image, composite_image
-
-# Create Gradio interface
-interface = gr.Interface(
-    fn=process_image,
-    inputs=gr.inputs.Image(type="pil"),
-    outputs=[
-        gr.outputs.Image(type="pil", label="Original Image"),
-        gr.outputs.Image(type="pil", label="Segmentation Mask"),
-        gr.outputs.Image(type="pil", label="Depth Map"),
-        gr.outputs.Image(type="pil", label="Blurred Background Effect"),
-    ],
-    title="Semantic Segmentation and Depth Estimation",
-    description="Upload an image to generate a segmentation mask, depth map, and blurred background effect."
-)
-
-# Launch the interface
-if __name__ == "__main__":
-    interface.launch()
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo.launch()