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import streamlit as st |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import os |
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from huggingface_hub import login |
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login(token=os.getenv("HUGGINGFACE_HUB_TOKEN")) |
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MODEL_NAME = "pymmdrza/TPT_v1" |
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@st.cache_resource |
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def load_model(): |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
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model = AutoModelForCausalLM.from_pretrained(MODEL_NAME) |
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return tokenizer, model |
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tokenizer, model = load_model() |
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st.title("Teste do Modelo TPT_v1") |
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input_text = st.text_area("Digite o texto de entrada:") |
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if st.button("Gerar Resposta"): |
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if input_text: |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=100) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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st.write("Resposta gerada:") |
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st.success(response) |
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else: |
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st.warning("Digite um texto para gerar uma resposta.") |
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