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| import streamlit as st | |
| from PIL import Image | |
| import torch | |
| from transformers import AutoImageProcessor | |
| import pandas as pd | |
| from transformers import ViTForImageClassification | |
| from transformers import VitsModel, AutoTokenizer | |
| import torch | |
| from IPython.display import Audio | |
| # Streamlit application title | |
| st.title("Phonically describe traffic signs") | |
| #Traffic Sign Classification | |
| model= ViTForImageClassification.from_pretrained('Rae1230/Traffic_Signs_Classification') | |
| processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224") | |
| uploaded_file = st.file_uploader("Choose a PNG image...", type="png", accept_multiple_files=False) | |
| if uploaded_file is not None: | |
| img = Image.open(uploaded_file) | |
| st.image(img, caption='Uploaded Image.', use_column_width=True) | |
| inputs = processor(img.convert('RGB'), return_tensors="pt") | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| img_class_idx=logits.argmax(-1).item() | |
| with open("labels.csv", "r") as file: | |
| df = pd.read_csv(file) | |
| num_col = df['ClassId'] | |
| text_col = df['Name'] | |
| text_value = text_col.loc[num_col == img_class_idx].values[0] | |
| st.header("Classified traffic sign:") | |
| st.write(text_value) | |
| #speech the Traffic Sign | |
| model2 = VitsModel.from_pretrained("facebook/mms-tts-eng") | |
| tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng") | |
| text = text_value | |
| inputs = tokenizer(text, return_tensors="pt") | |
| with torch.no_grad(): | |
| output = model2(**inputs).waveform | |
| st.header("Phonically describe this traffic sign:") | |
| st.audio(output.numpy(),sample_rate=model2.config.sampling_rate) |