Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, LayoutLMv3ImageProcessor
+
+model_name = "TusharGoel/LiLT-Document-QA"
+tokenizer = AutoTokenizer.from_pretrained(model_name, apply_ocr = True)
+image_processor = LayoutLMv3ImageProcessor()
+
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+model.eval()
+
+def qna(image, question):
+    
+    res = image_processor(image, apply_ocr = True)
+    words = res["words"][0]
+    boxes = res["boxes"][0]
+
+    encoding = tokenizer(question, words, boxes = boxes, return_token_type_ids=True, return_tensors="pt", truncation=True, padding="max_length")
+
+    word_ids = encoding.word_ids(0)
+    outputs = model(**encoding)
+
+    print(outputs)
+
+    start_scores = outputs.start_logits
+    end_scores = outputs.end_logits
+
+    start, end = word_ids[start_scores.argmax(-1).item()], word_ids[end_scores.argmax(-1).item()]
+
+    answer = " ".join(words[start : end + 1])
+    
+    
+    return answer
+
+
+img = gr.Image(source="upload", label="Image")
+question = gr.Text(label="Question")
+
+iface = gr.Interface(fn=qna, inputs=[img, question], outputs="text", title="LiLT - Document Question Answering")
+iface.launch()