git status# This is a combination of 2 commits.

app.py

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+import gradio as gr
+from indicnlp.transliterate.unicode_transliterate import UnicodeIndicTransliterator
+from transformers import VisionEncoderDecoderModel, AutoProcessor, AutoTokenizer
+from PIL import Image
+import torch
+from huggingface_hub import snapshot_download
+
+snapshot_download(repo_id = "QuickHawk/trocr-indic")
+
+ENCODER_MODEL_NAME = "facebook/deit-base-distilled-patch16-224"
+DECODER_MODEL_NAME = "ai4bharat/IndicBART"
+
+processor = AutoProcessor.from_pretrained(ENCODER_MODEL_NAME, use_fast=True)
+tokenizer = AutoTokenizer.from_pretrained(DECODER_MODEL_NAME, use_fast=True)
+
+model = VisionEncoderDecoderModel.from_pretrained(r"QuickHawk/trocr-indic")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+LANG_MAP = {
+    "as": "Assamese",
+    "bn": "Bengali",
+    "gu": "Gujarati",
+    "hi": "Hindi",
+    "kn": "Kannada",
+    "ml": "Malayalam",
+    "mr": "Marathi",
+    "or": "Odia",
+    "pa": "Punjabi",
+    "ta": "Tamil",
+    "te": "Telugu",
+    "ur": "Urdu"
+}
+
+bos_id = tokenizer._convert_token_to_id_with_added_voc("<s>")
+eos_id = tokenizer._convert_token_to_id_with_added_voc("</s>")
+pad_id = tokenizer._convert_token_to_id_with_added_voc("<pad>")
+
+def predict(image):
+
+    with torch.no_grad():
+        pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(device)
+        outputs_ids = model.generate(
+            pixel_values,
+            use_cache=True,
+            num_beams=4,
+            max_length=128,
+            min_length=1,
+            early_stopping=True,
+            pad_token_id=pad_id,
+            bos_token_id=bos_id,
+            eos_token_id=eos_id,
+            decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2en>")
+        )
+
+    lang_token = tokenizer.decode(outputs_ids[0][1])
+    lang = lang_token[2:-1]
+
+    caption = tokenizer.decode(outputs_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+    return UnicodeIndicTransliterator.transliterate(caption, "hi", lang), LANG_MAP[lang]
+
+gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs=[gr.Text(label = "Predicted Text"), gr.Text(label = "Predicted Language")]).launch()

requirements.txt

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+gradio
+torch
+torchvision
+transformers
+pillow
+indicnlp
+indic-nlp-library