import gradio as gr
import os
import json
import requests
from thinking_routines import thinking_routine_prompt, thinking_routine_examples
#Streaming endpoint
API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
#Huggingface provided GPT4 OpenAI API Key
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
#Inferenec function
def predict(system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {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)
title = """🎉📚 Super Learning Buddy for Primary School Students 🤖✨
"""
#Using info to add additional information about System message in GPT4
system_msg_info = """🤖✨ Choose your own learning buddy from the list! 📚🚀"""
# Modifying existing Gradio Theme
theme = gr.themes.Soft(primary_hue="indigo", secondary_hue="blue", neutral_hue="blue",
text_size=gr.themes.sizes.text_lg)
with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""",
theme=theme) as demo:
gr.HTML(title)
gr.HTML("""🎉🚀 Meet your fun Learning Buddy, ready for all thinking adventures! 🧠✨""")
with gr.Column(elem_id = "col_container"):
#GPT4 API Key is provided by Huggingface
with gr.Accordion(label="Select you Coach:", open=False):
system_msg = gr.Textbox(label="Choose the type of Coach to help guide you", info = system_msg_info, value="")
accordion_msg = gr.HTML(value="🚧 To set System message you will have to refresh the app", visible=False)
with gr.Accordion(label="Examples of Coaches using thinking routines:", open=False):
gr.Examples(
examples=[[thinking_routine_prompt(subject, routine)] for subject, routine in thinking_routine_examples],
inputs=system_msg,
)
chatbot = gr.Chatbot(label='Learning Buddy', elem_id="chatbot")
inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter")
state = gr.State([])
with gr.Row():
with gr.Column(scale=7):
b1 = gr.Button().style(full_width=True)
with gr.Column(scale=3):
server_status_code = gr.Textbox(label="Status code from OpenAI server", )
#top_p, temperature
with gr.Accordion("Parameters", open=False):
top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
chat_counter = gr.Number(value=0, visible=False, precision=0)
#Event handling
inputs.submit( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],) #openai_api_key
b1.click( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],) #openai_api_key
inputs.submit(set_visible_false, [], [system_msg])
b1.click(set_visible_false, [], [system_msg])
inputs.submit(set_visible_true, [], [accordion_msg])
b1.click(set_visible_true, [], [accordion_msg])
b1.click(reset_textbox, [], [inputs])
inputs.submit(reset_textbox, [], [inputs])
demo.queue(max_size=99, concurrency_count=20).launch(share=False)