Create Traffic_Signs_Classification

Traffic_Signs_Classification

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+import streamlit as st
+from PIL import Image
+import torch
+from torchvision import transforms
+from transformers import AutoImageProcessor
+import pandas as pd
+
+
+
+# Load the Traffic_Signs_Classification model pipeline
+classifier = pipeline("TrafficSigns-classification", model='Rae1230/Traffic_Signs_Classification', return_all_scores=True)
+# Streamlit application title
+st.title("Speech the Traffic Signs")
+
+
+uploaded_file = st.file_uploader("Choose a PNG image...", type="png", accept_multiple_files=False)
+if uploaded_file is not None:
+  image = Image.open(uploaded_file)
+  st.image(image, caption='Uploaded Image.', use_column_width=True)
+  processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
+  inputs = processor(image.convert('RGB'), return_tensors="pt")
+  result=classifier(inputs)
+    
+st.write(result)