create app.py

app.py

@@ -0,0 +1,37 @@
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+import gradio as gr
+
+qa_pipeline = pipeline("document-question-answering", model="RamzesIII/llmv2-docvqa-finetuned")
+
+ru_tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ru-en")
+ru_en_model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ru-en")
+
+en_tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ru")
+en_ru_model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-ru")
+
+def translate_ru2en(ru_question, ru_en_model, ru_tokenizer):
+    input_ids = ru_tokenizer.encode(ru_question, return_tensors="pt")
+    output_ids = ru_en_model.generate(input_ids, max_new_tokens=512)
+    en_question = ru_tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return en_question
+
+
+def translate_en2ru(en_answer, en_ru_model, en_tokenizer):
+    input_ids = en_tokenizer.encode(en_answer, return_tensors="pt")
+    output_ids = en_ru_model.generate(input_ids, max_new_tokens=512)
+    ru_answer = en_tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return ru_answer
+
+def ru_inference(image, ru_question):
+    en_question = translate_ru2en(ru_question, ru_en_model, ru_tokenizer)
+    en_answer = qa_pipeline(image=image, question=en_question)[0]['answer']
+    ru_answer = translate_en2ru(en_answer, en_ru_model, en_tokenizer)
+    return en_answer
+
+
+interface = gr.Interface(
+    fn=ru_inference, 
+    inputs=[gr.Image(type="pil"), gr.Textbox(label="Question")],
+    outputs=[gr.Text()],
+    title='Document answer questions'
+    ).launch(debug=True)