Spaces:
Runtime error
Runtime error
import gradio as gr | |
import subprocess | |
import json | |
import requests | |
from bs4 import BeautifulSoup | |
""" | |
General helper functions | |
""" | |
def strip_html_tags(html_text): | |
# Use BeautifulSoup to parse and clean HTML content | |
soup = BeautifulSoup(html_text, 'html.parser') | |
return soup.get_text() | |
""" | |
Padlet API Interactions | |
""" | |
def api_call(input_text): | |
#TODO: Refactor to be one function that can get OR post | |
curl_command = [ | |
'curl', '-s', '--request', 'GET', | |
'--url', f"https://api.padlet.dev/v1/boards/{board_id}?include=posts%2Csections", | |
'--header', 'X-Api-Key: pdltp_0e380a0de1ff32d77b12dbcc030b1373199b7525681ddc81bd1b9ef3e4e3dd49577a23', | |
'--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: | |
post_content = f"Subject: {subject}" | |
if body_html: | |
cleaned_body = strip_html_tags(body_html) | |
post_content += f"\nBody Text: {cleaned_body}" | |
post_contents.append(post_content) | |
return "\n\n".join(post_contents) if post_contents else "No post contents found." | |
except subprocess.CalledProcessError: | |
return "Error: Unable to fetch data using cURL." | |
def create_post(board_id, post_content): | |
curl_command = [ | |
'curl', '-s', '--request', 'POST', | |
'--url', f"https://api.padlet.dev/v1/boards/{board_id}/posts", | |
'--header', 'X-Api-Key: pdltp_0e380a0de1ff32d77b12dbcc030b1373199b7525681ddc81bd1b9ef3e4e3dd49577a23', | |
'--header', 'accept: application/vnd.api+json', | |
'--header', 'content-type: application/vnd.api+json', | |
'--data', | |
json.dumps({ | |
"data": { | |
"type": "post", | |
"attributes": { | |
"content": { | |
"subject": 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)}" | |
""" | |
LLM Functions | |
""" | |
#Streaming endpoint | |
API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream" | |
#Inference function | |
def predict(openai_gpt4_key, system_msg, api_result, top_p, temperature, chat_counter, chatbot=[], history=[]): | |
headers = { | |
"Content-Type": "application/json", | |
"Authorization": f"Bearer {openai_gpt4_key}" #Users will provide their own OPENAI_API_KEY | |
} | |
print(f"system message is ^^ {system_msg}") | |
if system_msg.strip() == '': | |
initial_message = [{"role": "user", "content": f"{inputs}"},] | |
multi_turn_message = [] | |
else: | |
initial_message= [{"role": "system", "content": system_msg}, | |
{"role": "user", "content": f"{inputs}"},] | |
multi_turn_message = [{"role": "system", "content": system_msg},] | |
if chat_counter == 0 : | |
payload = { | |
"model": "gpt-4", | |
"messages": initial_message , | |
"temperature" : 1.0, | |
"top_p":1.0, | |
"n" : 1, | |
"stream": True, | |
"presence_penalty":0, | |
"frequency_penalty":0, | |
} | |
print(f"chat_counter - {chat_counter}") | |
else: #if chat_counter != 0 : | |
messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},] | |
for data in chatbot: | |
user = {} | |
user["role"] = "user" | |
user["content"] = data[0] | |
assistant = {} | |
assistant["role"] = "assistant" | |
assistant["content"] = data[1] | |
messages.append(user) | |
messages.append(assistant) | |
temp = {} | |
temp["role"] = "user" | |
temp["content"] = inputs | |
messages.append(temp) | |
#messages | |
payload = { | |
"model": "gpt-4", | |
"messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}], | |
"temperature" : temperature, #1.0, | |
"top_p": top_p, #1.0, | |
"n" : 1, | |
"stream": True, | |
"presence_penalty":0, | |
"frequency_penalty":0,} | |
chat_counter+=1 | |
history.append(inputs) | |
print(f"Logging : payload is - {payload}") | |
# make a POST request to the API endpoint using the requests.post method, passing in stream=True | |
response = requests.post(API_URL, headers=headers, json=payload, stream=True) | |
print(f"Logging : response code - {response}") | |
token_counter = 0 | |
partial_words = "" | |
counter=0 | |
for chunk in response.iter_lines(): | |
#Skipping first chunk | |
if counter == 0: | |
counter+=1 | |
continue | |
# check whether each line is non-empty | |
if chunk.decode() : | |
chunk = chunk.decode() | |
# decode each line as response data is in bytes | |
if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']: | |
partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"] | |
if token_counter == 0: | |
history.append(" " + partial_words) | |
else: | |
history[-1] = partial_words | |
chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list | |
token_counter+=1 | |
yield chat, history, chat_counter, response # resembles {chatbot: chat, state: history} | |
#Resetting to blank | |
def reset_textbox(): | |
return gr.update(value='') | |
#to set a component as visible=False | |
def set_visible_false(): | |
return gr.update(visible=False) | |
#to set a component as visible=True | |
def set_visible_true(): | |
return gr.update(visible=True) | |
# Define the Gradio interface | |
iface = gr.Interface( | |
fn=predict, # Use 'predict' as the function | |
inputs=[ | |
gr.inputs.Textbox(label="OpenAI GPT4 Key", type="password", placeholder="sk.."), | |
gr.inputs.Textbox(label="System Message", default=""), | |
gr.inputs.Textbox(label="Input Board ID for api_call"), | |
gr.inputs.Textbox(label="Output Board ID for create_post"), | |
], | |
outputs=gr.outputs.Textbox(label="Summary"), | |
live=True, | |
title="Padlet API Caller with cURL and LLM", | |
description="Enter OpenAI GPT4 key, system message, input board ID for api_call, and output board ID for create_post", | |
) | |
# Add event handlers to call 'api_call' and 'create_post' when the "Generate Summary" and "Post Summary" buttons are clicked | |
iface.inputs[4].submit(api_call, [gr.inputs.Textbox]) | |
iface.inputs[4].click(api_call, [gr.inputs.Textbox]) | |
iface.inputs[5].submit(create_post, [gr.inputs.Textbox, gr.outputs.Textbox]) | |
iface.inputs[5].click(create_post, [gr.inputs.Textbox, gr.outputs.Textbox]) | |
iface.launch() |