Created
January 14, 2024 23:13
-
-
Save nickjevershed/757cd8b8b615654137f1c5ecd379ecc3 to your computer and use it in GitHub Desktop.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
#%% | |
import scraperwiki | |
import re | |
import requests | |
import json | |
import pandas as pd | |
import time | |
#%% | |
queryString = "* from comments" | |
queryResult = scraperwiki.sqlite.select(queryString) | |
results = [] | |
llama_url = 'http://localhost:11434/api/generate' | |
for row in queryResult: | |
start = time.time() | |
comment = row['comment'] | |
query = f'In the following text someone will specify their favourite word. Please return the favourite word from their text in the following format - "Favourite word: Word" - with no other words in your response. Here is the text: {comment}' | |
print(query) | |
data = { | |
"model": "llama2:13b", | |
"prompt": query, | |
"stream": False | |
} | |
r = requests.post(llama_url, data=json.dumps(data)) | |
word = r.json()['response'] | |
print(word) | |
row = {"word":word, "count":1, "comment":row['comment']} | |
results.append(row) | |
end = time.time() | |
print(end - start) | |
#%% | |
df = pd.DataFrame(results) | |
#%% | |
df.to_csv("llama-words.csv") | |
#%% | |
df['word'] = df['word'].str.strip() | |
#%% | |
#%% | |
df['word2'] = df['word'].str.split("Favourite word: ").str[-1] | |
#%% | |
df['word2'] = df['word2'].str.replace("#", "") | |
#%% | |
df['word2'] = df['word2'].str.lower() | |
#%% | |
sum_df = df[df['word_check'] != False] | |
blah = df[df['word_check'] == False] | |
summary = sum_df.groupby('word2').count() | |
#%% | |
summary.to_csv('summary.csv') | |
#%% | |
def checkWord(row): | |
if row['word2'] in row['comment'].lower(): | |
return True | |
else: | |
return False | |
df['word_check'] = df.apply(checkWord, axis=1) | |
#%% | |
df.to_csv("llama-words2.csv") | |
#%% | |
#%% | |
dg = df.groupby(['word']).count() | |
#%% | |
dg.to_csv('words.csv') | |
# %% |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment