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from datasets import load_dataset
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig, TrainingArguments, Trainer
import torch
import time
import evaluate
import pandas as pd
import numpy as np
import streamlit as st
st.title('Code Generation')
huggingface_dataset_name = "red1xe/code_instructions"
dataset = load_dataset(huggingface_dataset_name)
if st.button("Load Model"):
with st.spinner('Loading Model...'):
model_name='google/flan-t5-base'
original_model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_name)
x = st.slider('Select a sample', 0, 1000, 200)
if st.button("Show Sample"):
index = x
input = dataset['test'][index]['input']
instruction = dataset['test'][index]['instruction']
output = dataset['test'][index]['output']
prompt = f"""
Answer the following question.
{input} {instruction}
Answer:
"""
inputs = tokenizer(prompt, return_tensors='pt')
outputs = tokenizer.decode(
original_model.generate(
inputs["input_ids"],
max_new_tokens=200,
)[0],
skip_special_tokens=True
)
dash_line = '-'.join('' for x in range(100))
st.write(dash_line)
st.write(f'INPUT PROMPT:\n{prompt}')
st.write(dash_line)
st.write(f'BASELINE HUMAN SUMMARY:\n{output}\n')
st.write(dash_line)
st.write(f'MODEL GENERATION - ZERO SHOT:\n{outputs}') |