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import streamlit as st |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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@st.cache(allow_output_mutation=True) |
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def load_model(): |
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model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat") |
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tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-chat", use_fast=False) |
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return model, tokenizer |
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model, tokenizer = load_model() |
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st.title("Finance Chatbot") |
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user_input = st.text_area("Enter your query:") |
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if st.button("Submit"): |
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if user_input: |
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prompt = f"<s>[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n{user_input} [/INST]" |
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inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device) |
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outputs = model.generate(input_ids=inputs, max_length=4096)[0] |
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answer_start = int(inputs.shape[-1]) |
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pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True) |
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st.write("### Assistant Output:") |
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st.write(pred) |
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else: |
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st.write("Please enter a query.") |
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