Spaces:
Runtime error
Runtime error
File size: 7,517 Bytes
479c5b5 d1c6587 ebf808d 8f66103 0f61c06 b516947 8f66103 b516947 479c5b5 8f66103 d1c6587 b516947 8f66103 d1c6587 b516947 d1c6587 ebf808d b516947 ebf808d 37ae3c7 b516947 37ae3c7 ebf808d 37ae3c7 d1c6587 b516947 8f66103 c0529d2 d1c6587 8f66103 c0529d2 d1c6587 8f66103 c0529d2 d1c6587 479c5b5 c0529d2 8f66103 479c5b5 |
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 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 |
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() |