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from transformers import MBartForConditionalGeneration, MBart50Tokenizer
import gradio as gr
import train as tr
'''
# Load the model and tokenizer
model_name = "LocalDoc/mbart_large_qa_azerbaijan"
tokenizer = MBart50Tokenizer.from_pretrained(model_name, src_lang="en_XX", tgt_lang="az_AZ")
model = MBartForConditionalGeneration.from_pretrained(model_name)
'''
def answer_question(text, question):
# Prepare input text
input_text = f"context: {text} question: {question}"
inputs = tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=False, padding="max_length")
# Generate answer
outputs = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_length=1024,
num_beams=5,
early_stopping=True
)
# Decode the answer
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
return answer
demo = gr.Interface(
fn=answer_question,
inputs=["text", "text"],
outputs=["text"]
)
#demo.launch()
tr.init() |