Create app.py

app.py

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+import torch
+from PIL import Image
+from transformers import (AutoTokenizer, VisionEncoderDecoderModel,
+                          ViTFeatureExtractor)
+from data_loaders import modify_dataset
+import pandas as pd
+from tqdm import tqdm
+import gradio as gr
+
+if torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+
+encoder_checkpoint = "google/vit-base-patch16-224-in21k"
+decoder_checkpoint = "distilgpt2"
+model_checkpoint = "gagan3012/ViTGPT2_vizwiz"
+feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
+tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
+model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
+
+def predict(image):
+    clean_text = lambda x: x.replace("<|endoftext|>", "").split("\n")[0]
+    sample = feature_extractor(image, return_tensors="pt").pixel_values.to(device) 
+    caption_ids = model.generate(sample, max_length=50)[0]
+    caption_text = clean_text(tokenizer.decode(caption_ids))
+    return caption_text
+  
+inputs = [
+    gr.inputs.Image(type="pil", label="Original Image")
+]
+
+outputs = [
+    gr.outputs.Textbox(label = 'Caption')
+]
+
+title = "Image Captioning using ViT + GPT2"
+description = "ViT and GPT2 are used to generate Image Caption for the uploaded images"
+article = " <a href='https://huggingface.co/gagan3012/ViTGPT2_vizwiz'>Model Repo on Hugging Face Model Hub</a>"
+examples = [
+    ["people-walking-street-pedestrian-crossing-traffic-light-city.jpeg"],
+    ["elonmusk.jpeg"]
+]
+
+gr.Interface(
+    predict,
+    inputs,
+    outputs,
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    theme="huggingface",
+).launch(debug=True, enable_queue=True)