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Runtime error
| import torch | |
| import re | |
| import gradio as gr | |
| from pathlib import Path | |
| from transformers import AutoTokenizer, AutoFeatureExtractor, VisionEncoderDecoderModel | |
| def predict(image, max_length=64, num_beams=4): | |
| image = image.convert('RGB') | |
| pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values | |
| pixel_values = pixel_values.to(device) | |
| with torch.no_grad(): | |
| text = tokenizer.decode(model.generate(pixel_values.cpu())[0]) | |
| text = text.replace('<|endoftext|>', '').split('\n') | |
| return text[0] | |
| model_path = "team-indain-image-caption/hindi-image-captioning" | |
| device = "cpu" | |
| # Load model. | |
| model = VisionEncoderDecoderModel.from_pretrained(model_path) | |
| model.to(device) | |
| print("Loaded model") | |
| feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") | |
| print("Loaded feature_extractor") | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| print("Loaded tokenizer") | |
| title = "Hindi Image Captioning" | |
| description = "" | |
| input = gr.inputs.Image(label="Image to search", type = 'pil', optional=False) | |
| output = gr.outputs.Textbox(type="auto",label="Captions") | |
| article = "This huggingface presents a demo for Image captioning in Hindi built with VIT Encoder and GPT2 Decoder" | |
| example = ["./examples/example_{i}.jpg" for i in range(1,6)] | |
| interface = gr.Interface( | |
| fn=predict, | |
| inputs = input, | |
| theme="grass", | |
| outputs=output, | |
| title=title, | |
| description=article, | |
| ) | |
| interface.launch(share = True) |