File size: 1,669 Bytes
7f51a5a cb23e9a 7f51a5a 44131c9 7f51a5a 2856ccc 44131c9 7f51a5a 85c4439 b61717e f100939 9b2bdb3 f100939 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, TrainingArguments, Trainer
import torch
import time
import evaluate
import pandas as pd
import numpy as np
from huggingface_hub import login
import streamlit as st
st.set_page_config(
page_title="Code Generation",
page_icon="🤖",
layout="wide",
initial_sidebar_state="expanded",
)
login(token='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', add_to_git_credential=True)
st.title("Code Generation")
huggingface_dataset_name = "red1xe/code_instructions"
dataset = load_dataset(huggingface_dataset_name)
model_name='bigcode/starcoder'
tokenizer = AutoTokenizer.from_pretrained(model_name)
original_model = AutoModelForCausalLM.from_pretrained(model_name)
x = st.slider(label='Select a sample', min_value=0, max_value=1000, value=500, step=10)
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}') |