Add application file
Browse files
app.py
CHANGED
@@ -9,8 +9,48 @@ import numpy as np
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import streamlit as st
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st.title('Code Generation')
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huggingface_dataset_name = "red1xe/code_instructions"
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st.
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st.
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import streamlit as st
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st.title('Code Generation')
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huggingface_dataset_name = "red1xe/code_instructions"
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if st.button("Load Dataset"):
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with st.spinner('Loading Dataset...'):
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dataset = load_dataset(huggingface_dataset_name)
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if st.button("Show Dataset"):
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st.write(dataset)
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if st.button("Load Model"):
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with st.spinner('Loading Model...'):
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model_name='google/flan-t5-base'
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original_model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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x = st.slider('Select a sample', 0, 1000, 200)
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if st.button("Show Sample"):
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index = x
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input = dataset['test'][index]['input']
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instruction = dataset['test'][index]['instruction']
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output = dataset['test'][index]['output']
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prompt = f"""
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Answer the following question.
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{input} {instruction}
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Answer:
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"""
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inputs = tokenizer(prompt, return_tensors='pt')
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outputs = tokenizer.decode(
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original_model.generate(
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inputs["input_ids"],
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max_new_tokens=200,
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)[0],
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skip_special_tokens=True
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)
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dash_line = '-'.join('' for x in range(100))
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st.write(dash_line)
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st.write(f'INPUT PROMPT:\n{prompt}')
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st.write(dash_line)
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st.write(f'BASELINE HUMAN SUMMARY:\n{output}\n')
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st.write(dash_line)
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st.write(f'MODEL GENERATION - ZERO SHOT:\n{outputs}')
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