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from transformers import pipeline |
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import streamlit as st |
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@st.cache_resource |
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def context_text(text): return f"### Context\n{text}\n\n### Answer" |
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@st.cache_resource |
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def load_pipe(): |
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return pipeline("text-generation", model="MSey/tiny_CaLL_r10_O1_f10_LT_c1022") |
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pipe = load_pipe() |
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st.header("Test Environment for tiny_CaLL_r10_O1_f10_LT_c1022") |
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user_input = st.text_input("Enter your Prompt here:", "") |
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contexted_ipnut = context_text(user_input) |
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context_len = len(contexted_ipnut) |
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if user_input: |
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with st.spinner('Generating response...'): |
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response = pipe(contexted_ipnut, max_new_tokens = 200, num_return_sequences=1) |
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generated_text = response[0]['generated_text'][context_len:] |
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st.write("Generated Text:") |
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st.markdown(generated_text) |
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st.text(generated_text) |
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