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1006bbb 71f0327 1006bbb 71f0327 1006bbb 71f0327 1006bbb 71f0327 1006bbb 8d2ba63 1006bbb 71f0327 1006bbb eca526b 71f0327 eca526b 1006bbb 71f0327 1006bbb 71f0327 | 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 | # import gradio as gr
# from huggingface_hub import InferenceClient
# def respond(
# message,
# history: list[dict[str, str]],
# system_message,
# max_tokens,
# temperature,
# top_p,
# hf_token: gr.OAuthToken,
# ):
# """
# 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
# """
# client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
# messages = [{"role": "system", "content": system_message}]
# messages.extend(history)
# messages.append({"role": "user", "content": message})
# response = ""
# for message in client.chat_completion(
# messages,
# max_tokens=max_tokens,
# stream=True,
# temperature=temperature,
# top_p=top_p,
# ):
# choices = message.choices
# token = ""
# if len(choices) and choices[0].delta.content:
# token = choices[0].delta.content
# response += token
# yield response
# """
# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
# """
# chatbot = 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)",
# ),
# ],
# )
# with gr.Blocks() as demo:
# with gr.Sidebar():
# gr.LoginButton()
# chatbot.render()
# if __name__ == "__main__":
# demo.launch()
import gradio as gr
from ollama import Client
# Define your preferred local model
MODEL_NAME = "gemma4:e2b"
def chat_stream(message, history):
# Format history into Ollama's expected structure
messages = []
for user_msg, bot_msg in history:
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": message})
# Stream the response
client = Client(host='https://thanthamky-ollama-api.hf.space')
response = client.chat(model=MODEL_NAME, messages=messages, stream=True)
partial_message = ""
for chunk in response:
partial_message += chunk['message']['content']
yield partial_message
# Launch the Gradio chat interface
demo = gr.ChatInterface(
fn=chat_stream,
title="Local Chatbot with Ollama & Gradio",
description=f"Running {MODEL_NAME} on your local machine."
)
if __name__ == "__main__":
demo.launch()
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