first commit

app.py

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+from transformers import ViTImageProcessor, ViTForImageClassification
+from PIL import Image
+import requests
+import os 
+import gradio as gr
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+
+def predict(img) -> Tuple[Dict, float]:
+    start_time = timer()
+    processor = ViTImageProcessor.from_pretrained('bazyl/gtsrb-model')
+    model = ViTForImageClassification.from_pretrained('bazyl/gtsrb-model')
+    inputs = processor(images=img, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    print("Predicted class:", model.config.id2label[predicted_class_idx])
+
+title = "GTSRB - German Traffic Sign Recognition by Bazyl Horsey"
+description = "CNN created for the GTSRB Dataset, achieved 99.93% test accuracy"
+
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create Gradio interface 
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(num_top_classes=5, label="Predictions"),
+        gr.Number(label="Prediction time (s)"),
+    ],
+    examples=example_list,
+    title=title,
+    description=description,
+)
+
+# Launch the app!
+demo.launch()

requirements.txt

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+gradio==3.28.3
+Pillow==9.5.0
+Requests==2.30.0
+transformers==4.28.1