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import gradio as gr
import http
import ssl
import json
import warnings
warnings.filterwarnings("ignore")
def retrieve_api_key(url):
context = ssl.create_default_context()
context.check_hostname = True
conn = http.client.HTTPSConnection(url, context=context)
conn.request("GET", "/admin/api-keys/")
api_key_response = conn.getresponse()
api_keys_data = (
api_key_response.read().decode("utf-8").replace("\n", "").replace("\t", "")
)
api_keys_json = json.loads(api_keys_data)
api_key = api_keys_json[0]["api_key"]
conn.close()
return api_key
def get_benchmark_uids(num_miner):
url="test.neuralinternet.ai"
api_key = retrieve_api_key(url)
context = ssl.create_default_context()
context.check_hostname = True
conn = http.client.HTTPSConnection(url, context=context)
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}",
"Endpoint-Version": "2023-05-19",
}
conn.request("GET", f"/top_miner_uids?n={num_miner}", headers=headers)
miner_response = conn.getresponse()
miner_data = (
miner_response.read().decode("utf-8").replace("\n", "").replace("\t", "")
)
uids = json.loads(miner_data)
return uids
def retrieve_response(payload):
url="d509-65-108-32-175.ngrok-free.app"
api_key = retrieve_api_key(url)
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}",
"Endpoint-Version": "2023-05-19",
}
payload = json.dumps(payload)
context = ssl.create_default_context()
context.check_hostname = True
conn = http.client.HTTPSConnection(url, context=context)
conn.request("POST", "/chat", payload, headers)
init_response = conn.getresponse()
init_data = init_response.read().decode("utf-8").replace("\n", "").replace("\t", "")
init_json = json.loads(init_data)
response_dict = dict()
for choice in init_json['choices']:
uid = choice['uid']
resp = choice['message']['content']
resp = resp.replace("\n", "").replace("\t", "")
response_dict[uid] = resp
response_text = '\n\n'.join([f'"{key}": "{value}"' for key, value in response_dict.items()])
return response_text
def interface_fn(system_prompt, optn, arg, user_prompt):
if len(system_prompt) == 0:
system_prompt = "You are an AI Assistant, created by bittensor and powered by NI(Neural Internet). Your task is to provide consise response to user's prompt"
messages = [{"role": "system", "content": system_prompt},{"role": "user", "content": user_prompt}]
payload = dict()
if optn == 'TOP':
if int(arg) > 30:
arg = 30
payload['top_n'] = int(arg)
payload['messages'] = messages
response = retrieve_response(payload)
return response
elif optn == 'BENCHMARK':
if int(arg) > 30:
arg = 30
uids = get_benchmark_uids(int(arg))
payload['uids'] = uids
payload['messages'] = messages
response = retrieve_response(payload)
return response
else:
uids = list()
if ',' in arg:
uids = [int(x) for x in arg.split(',')]
else:
uids = [arg]
payload['uids'] = uids
payload['messages'] = messages
response = retrieve_response(payload)
return response
interface = gr.Interface(
fn=interface_fn,
inputs=[
gr.inputs.Textbox(label="System Prompt", optional=True),
gr.inputs.Dropdown(["TOP", "BENCHMARK", "UIDs"], label="Select Function"),
gr.inputs.Textbox(label="Arguement"),
gr.inputs.Textbox(label="Enter your question")
],
outputs=gr.outputs.Textbox(label="Model Responses"),
title="Explore Bittensor Miners",
description="Enter parameters as per you want and get response",
examples=[["Your task is to provide consise response of user prompts", "TOP", 5, 'What is Bittensor?']
,["Your task is to provide accurate, lengthy response with good lexical flow", "BENCHMARK", 5, "What is neural network and how its feeding mechanism works?"],
["Act like you're in the technology field for 10+ year and give unbiased opinion", "UIDs", '975,517,906,743,869' , "What are the potential ethical concerns surrounding artificial intelligence and machine learning in healthcare?"]])
interface.launch(enable_queue=True) |