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import gradio as gr
#from huggingface_hub import InferenceClient
import os
import requests
PROMPT_TEMPLATE = """You are a friendly Chatbot."""
system_prompt=PROMPT_TEMPLATE
specialtoken = os.getenv("SPECIALTOKEN")
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
#TODO remove max_tokens,temp,top_p to make it by default
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
payload = {
"model": "openai",
"messages": messages,
#"response_format": { "type": "json_object" },
#"tools": tools,
#"tool_choice": "auto",
#"stream": True,
}
resp = requests.post(
specialtoken,
json=payload,
headers={"Content-Type": "application/json"}
)
response_messages=resp.json()["choices"] #[0]["message"]["content"]
response = ""
for message in response_messages:
token = message["message"]["content"]
response += token
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
#gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
#gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
#gr.Slider(minimum=0.1,maximum=1.0,value=0.95,step=0.05,label="Top-p (nucleus sampling)",),
],
)
if __name__ == "__main__":
demo.launch()
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