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| import streamlit as st | |
| import datasets | |
| from datasets import load_dataset | |
| import pandas as pd | |
| from streamlit.components.v1 import html | |
| from streamlit import markdown | |
| import re | |
| import os | |
| import time | |
| import json | |
| st.title('StackOverflow Question Demo') | |
| library = st.radio('Select a library', ('numpy', 'tensorflow+ pytorch', 'scipy', 'scikit-learn', 'pandas')) | |
| question_path = './{}.txt'.format(library) | |
| # loading stackoverflow questions. | |
| # using huggingface load_dataset function. | |
| # not done yet | |
| #@st.cache | |
| #def load_data(path): | |
| # return load_dataset('text', data_files = path, cache_dir = './data') | |
| intro = {'numpy': ''' | |
| #### Setup | |
| temperature = 0.7, topP = 0.95, turns = 10 | |
| #### Prompt: | |
| Problem: | |
| …… | |
| A: | |
| <code> | |
| …… | |
| ###BEGIN SOLUTION | |
| [insert] | |
| ###END SOLUTION | |
| … | |
| </code> | |
| A0: change example | |
| A1: change logits(decimal places, array, etc) | |
| A2: change output type (array -> dict, etc) | |
| A3: analogy | |
| A4: dimension(index) involved | |
| A5: inverted operation | |
| A6: order | |
| A7: ±condition/operation | |
| combinations involved, only show the highest level. | |
| ''', | |
| 'scipy': | |
| ''' | |
| #### Setup | |
| temperature = 0.7, topP = 0.95, 10 attempts. | |
| #### Prompt: | |
| Problem: | |
| …… | |
| A: | |
| <code> | |
| …… | |
| ###BEGIN SOLUTION | |
| [insert] | |
| ###END SOLUTION | |
| … | |
| </code> | |
| Origin: original question from stackoverflow(might be specified or simplified) | |
| Function: Let model fill in a function. | |
| A1: paraphrasing, seems not effective to Codex. | |
| A2: change example | |
| A3: analogy(min->max, column->row, etc) | |
| A6: result type constraint. | |
| A7: ±condition/operation | |
| ''' | |
| } | |
| hyper_links = {'numpy':'https://docs.google.com/document/d/1WjMXfe-zV5VvKfbUnyxauTBciPB1Bp82baaIrG3XffM/edit#', | |
| 'scipy': 'https://docs.google.com/document/d/1u_rGiLrLbH9Ac_OueTbmDFyLlWOtB0U56Ertp8ggW1Q/edit'} | |
| st.write(intro[library]) | |
| st.write('If the demo seems a little confusing, feel free to check the document.', hyper_links[library]) | |
| dataset = [] | |
| #dataset = load_data(question_path) | |
| with open(question_path) as f: | |
| lines = f.readlines() | |
| question = '' | |
| temp = {} | |
| tag = '' | |
| for line in lines: | |
| if line == 'Origin:\n' or line == 'Function:\n' or re.match(r'A\d:\n', line): | |
| if not tag: | |
| tag = line[:-2] | |
| else: | |
| temp[tag] = question | |
| question = '' | |
| tag = line[:-2] | |
| elif re.match(r'\d*\.\n', line): | |
| if tag: | |
| temp[tag] = question | |
| dataset.append(temp) | |
| question = '' | |
| tag = '' | |
| temp = {} | |
| else: | |
| if tag: | |
| question += line + '\n' | |
| temp[tag] = question | |
| dataset.append(temp) | |
| # Select index | |
| number = st.number_input("Insert a index: range from", | |
| min_value = 0, max_value = len(dataset) - 1) | |
| st.write('The current index is ', number) | |
| data_index = int(number) | |
| # Selece modification | |
| options = tuple(dataset[data_index].keys()) | |
| modification = st.radio('Modification:', | |
| options = options | |
| ) | |
| st.write(dataset[data_index][modification]) | |