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import streamlit as st |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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import os |
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st.title("AI Accountant - Prompt-Based ERP Entry") |
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model_path = os.path.abspath("finetuned-flan-t5") |
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tokenizer = AutoTokenizer.from_pretrained(model_path, local_files_only=True) |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_path, local_files_only=True) |
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user_input = st.text_area("Enter accounting transaction:") |
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if st.button("Generate Entry"): |
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inputs = tokenizer(user_input, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=128) |
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result = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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st.json(eval(result)) |
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