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import gradio as gr | |
import subprocess | |
import json | |
import requests | |
import re | |
import pandas as pd | |
import openai | |
from bs4 import BeautifulSoup | |
# Simple function to strip html | |
def strip_html_tags(html_text): | |
# Use BeautifulSoup to parse and clean HTML content | |
soup = BeautifulSoup(html_text, 'html.parser') | |
return soup.get_text() | |
def html_posts_to_table(html_posts): | |
subject_pattern = r"Subject: (.*?)\n" | |
body_text_pattern = r"Body Text: (.*?)\n" | |
subjects = re.findall(subject_pattern, html_posts) | |
body_texts = re.findall(body_text_pattern, html_posts) | |
data = { | |
'Subject': subjects, | |
'Body Text': body_texts | |
} | |
df = pd.DataFrame(data) | |
return(df) | |
def api_call(board_id, api_key): | |
curl_command = [ | |
'curl', '-s', '--request', 'GET', | |
'--url', f"https://api.padlet.dev/v1/boards/{board_id}?include=posts%2Csections", | |
'--header', f"X-Api-Key: {api_key}", | |
'--header', 'accept: application/vnd.api+json' | |
] | |
try: | |
response = subprocess.check_output(curl_command, universal_newlines=True) | |
response_data = json.loads(response) | |
# Extract the contents of all posts, stripping HTML tags from bodyHtml | |
posts_data = response_data.get("included", []) | |
post_contents = [] | |
for post in posts_data: | |
if post.get("type") == "post": | |
attributes = post.get("attributes", {}).get("content", {}) | |
subject = attributes.get("subject", "") | |
body_html = attributes.get("bodyHtml", "") | |
if subject: | |
cleaned_body = strip_html_tags(body_html) | |
post_contents.append({"subject": subject, "content": cleaned_body}) | |
if post_contents: | |
df = pd.DataFrame(post_contents) | |
return df | |
else: | |
return pd.DataFrame({"subject": ["No post contents found."], "content": [""]}) | |
except subprocess.CalledProcessError: | |
return pd.DataFrame({"subject": ["Error: Unable to fetch data using cURL."], "content": [""]}) | |
def create_post(subject, post_content, board_id, api_key): | |
curl_command = [ | |
'curl', '-s', '--request', 'POST', | |
'--url', f"https://api.padlet.dev/v1/boards/{board_id}/posts", | |
'--header', f"X-Api-Key: {api_key}", | |
'--header', 'accept: application/vnd.api+json', | |
'--header', 'content-type: application/vnd.api+json', | |
'--data', | |
json.dumps({ | |
"data": { | |
"type": "post", | |
"attributes": { | |
"content": { | |
"subject": subject, | |
"body": post_content | |
} | |
} | |
} | |
}) | |
] | |
try: | |
response = subprocess.check_output(curl_command, universal_newlines=True) | |
response_data = json.loads(response) | |
return "Post created successfully." | |
except subprocess.CalledProcessError as e: | |
return f"Error: Unable to create post - {str(e)}" | |
def posts_to_prompt(padlet_posts): | |
post_prompt = padlet_posts.apply(lambda row: f"{row['subject']} {row['content']}", axis=1).str.cat(sep=', ') | |
return post_prompt | |
def remove_html_tags(text): | |
# Use a regular expression to remove HTML tags | |
clean = re.compile('<.*?>') | |
return re.sub(clean, '', text) | |
def summarize_padlet_posts(padlet_posts, openai_api_key, system_prompt): | |
# Concatenate padlet post df | |
post_prompt = posts_to_prompt(padlet_posts) | |
# Set the system prompt with more specific instructions | |
system_prompt = system_prompt | |
# Set the prompt for the GPT-3.5 model | |
prompt = system_prompt + "\n" + post_prompt # Added a newline after system_prompt | |
try: | |
# Make the API call to GPT-3.5 | |
response = openai.Completion.create( | |
engine="text-davinci-003", # GPT-3.5 engine | |
prompt=prompt, | |
max_tokens=1000, # Limit response length for concise summaries | |
api_key=openai_api_key, | |
temperature=0.5 # Adjust temperature as needed | |
) | |
# Extract and return the summary, removing leading newlines and HTML tags | |
summary = response.choices[0].text.lstrip('\n') | |
summary = remove_html_tags(summary) | |
return summary | |
except Exception as e: | |
return f"Error: {str(e)}" | |
def summarize_padlets(input_board_id, output_board_id, padlet_api, openai_api, system_prompt): | |
posts_to_summarize = api_call(input_board_id, padlet_api) | |
summary = summarize_padlet_posts(posts_to_summarize, openai_api, system_prompt) | |
create_post("Summary",summary, output_board_id, padlet_api) | |
return(summary) | |
iface = gr.Interface( | |
fn=summarize_padlets, | |
inputs=[ | |
gr.inputs.Textbox(label="Input Board ID"), | |
gr.inputs.Textbox(label="Output Board ID"), | |
gr.inputs.Textbox(label="Padlet API Key", type="password"), | |
gr.inputs.Textbox(label="OpenAI API Key", type="password", placeholder="sk.."), | |
gr.inputs.Textbox(label="System Prompt", default = "You are an AI assistant tasked with summarizing the main points of the following Padlet posts. Please provide a concise summary of the posts based on their content.") | |
], | |
outputs=gr.outputs.Textbox(label="Summary"), | |
live=False, # Set to True to show the result without clicking a button | |
title="Padlet Summarization", | |
description="Summarize Padlet posts and create a summary post on another board using OpenAI GPT3.5.", | |
) | |
# Run the Gradio interface | |
iface.launch() |