Initial files

app.py

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+import matplotlib.pyplot as plt
+import gradio as gr
+from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation
+import torch
+
+def classify(im):
+  inputs = extractor(images=im, return_tensors="pt").to("cuda")
+  outputs = model(**inputs)
+  logits = outputs.logits
+  classes = logits[0].detach().cpu().numpy().argmax(axis=0)
+  colors = np.array([[128,0,0], [128,128,0], [0, 0, 128],     [128,0,128], [0, 0, 0]])
+  return colors[classes]
+  
+
+
+interface = gr.Interface(fn=classify,
+             inputs=gr.inputs.Image(type="pil"),
+             outputs=gr.Label(num_top_classes=3),
+             title="Self Driving Car App- Semantic Segmentation",
+             description="This is a self driving car app using Semantic Semendation as part of week 2 end to end vision application project on CoRise.",
+             examples=["https://datasets-server.huggingface.co/assets/segments/sidewalk-semantic/--/segments--sidewalk-semantic-2/train/3/pixel_values/image.jpg",
+                       "https://datasets-server.huggingface.co/assets/segments/sidewalk-semantic/--/segments--sidewalk-semantic-2/train/5/pixel_values/image.jpg",
+                       "https://datasets-server.huggingface.co/assets/segments/sidewalk-semantic/--/segments--sidewalk-semantic-2/train/20/pixel_values/image.jpg"])# FILL HERE
+
+interface.launch()

requirements.txt

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+matplotlib
+gradio
+torch
+transformers