File size: 2,827 Bytes
e23ea2d 0f45270 e23ea2d 4f438a9 e23ea2d 80a9208 e23ea2d 80a9208 e23ea2d 0f45270 e23ea2d 0f45270 f9df74e b96ceec 0f45270 e23ea2d 0f45270 e23ea2d b8c70f9 e23ea2d |
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 |
import gradio as gr
from gradio_client import Client
import os
import requests
tulu = "https://tonic1-tulu.hf.space/--replicas/t5vxm/"
HF_TOKEN = os.getenv("HF_TOKEN")
HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
def build_input_prompt(message, chatbot, system_prompt):
"""
Constructs the input prompt string from the chatbot interactions and the current message.
"""
input_prompt = "<|system|>\n" + system_prompt + "</s>\n<|user|>\n"
for interaction in chatbot:
input_prompt = input_prompt + str(interaction[0]) + "</s>\n<|assistant|>\n" + str(interaction[1]) + "\n</s>\n<|user|>\n"
input_prompt = input_prompt + str(message) + "</s>\n<|assistant|>"
return input_prompt
def post_request_beta(payload):
"""
Sends a POST request to the predefined Tulu URL and returns the JSON response.
"""
response = requests.post(tulu, headers=HEADERS, json=payload)
response.raise_for_status() # Will raise an HTTPError if the HTTP request returned an unsuccessful status code
return response.json()
def predict_beta(message, chatbot=[], system_prompt=""):
client = Client(tulu) # Assuming Client is properly defined and tulu is a valid argument
# Build the input prompt
input_prompt = build_input_prompt(message, chatbot, system_prompt) # Ensure this function is defined
try:
# Adjust these parameters as needed
max_new_tokens = 1200
temperature = 0.4
top_p = 0.9
repetition_penalty = 0.9
advanced = True
# Making the prediction
result = client.predict(
input_prompt, # Using the built input prompt
max_new_tokens,
temperature,
top_p,
repetition_penalty,
advanced,
fn_index=0
)
# Extracting the response
if result is not None and len(result) > 0:
bot_message = result[0] # Assuming the response is in the first element
return bot_message
else:
raise gr.Error("No response received from the model.")
except Exception as e:
error_msg = f"An error occurred: {str(e)}"
raise gr.Error(error_msg)
def test_preview_chatbot(message, history):
response = predict_beta(message, history, SYSTEM_PROMPT)
text_start = response.rfind("<|assistant|>", ) + len("<|assistant|>")
response = response[text_start:]
return response
welcome_preview_message = f"""
Welcome to **{TITLE}**! Say something like:
''{EXAMPLE_INPUT}''
"""
chatbot_preview = gr.Chatbot(layout="panel", value=[(None, welcome_preview_message)])
textbox_preview = gr.Textbox(scale=7, container=False, value=EXAMPLE_INPUT)
demo = gr.ChatInterface(test_preview_chatbot, chatbot=chatbot_preview, textbox=textbox_preview)
demo.launch() |