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| from transformers import ViTFeatureExtractor, ViTForImageClassification | |
| import gradio as gr | |
| from datasets import load_dataset | |
| import torch | |
| #dataset = load_dataset("cifar100") | |
| #image = dataset["train"]["fine_label"] | |
| #print("load and train dataset \n") | |
| feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224') | |
| print("feature extractor \n") | |
| model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') | |
| print("load model \n") | |
| def classify(image): | |
| inputs = feature_extractor(images=image, return_tensors="pt") | |
| print("define input \n") | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| # model predicts one of the 1000 ImageNet classes | |
| print("prediction \n") | |
| predicted_class_idx = logits.argmax(-1).item() | |
| return model.config.id2label[predicted_class_idx] | |
| def image2speech(image): | |
| print("tts \n") | |
| try: | |
| txt = classify(image) | |
| except: | |
| txt = "No object detected" | |
| return fastspeech(txt), txt | |
| print("load tts interface \n") | |
| fastspeech = gr.Interface.load("huggingface/facebook/fastspeech2-en-ljspeech") | |
| print("sets input and outputs \n") | |
| camera = gr.inputs.Image(label="Image from your camera", source="webcam") | |
| read = gr.outputs.Textbox(type="auto", label="Text") | |
| speak = gr.outputs.Audio(type="auto", label="Speech") | |
| print("define interface \n") | |
| app = gr.Interface(fn=image2speech, | |
| inputs=camera, | |
| live=True, | |
| description="Takes a snapshot of an object, identifies it, and then tell you what it is. \n Intended use is to help the visually impaired. Models and dataset used is listed on the linked models and dataset", | |
| outputs=[speak, read], | |
| examples=["remotecontrol.jpg", "calculator.jpg", "cellphone.jpg"]) | |
| print("launch interface \n") | |
| app.launch(cache_examples=True) |