Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,854 Bytes
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import gradio as gr
from huggingface_hub import InferenceClient
import spaces
import torch
import os
from huggingface_hub import login
from PIL import Image
from transformers import AutoProcessor, Gemma3ForConditionalGeneration
"""
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("HuggingFaceH4/zephyr-7b-beta")
duration=None
login(token = os.getenv('gemma'))
ckpt = "google/gemma-3-4b-it"
model = Gemma3ForConditionalGeneration.from_pretrained(
ckpt, device_map="auto", torch_dtype=torch.bfloat16,
)
processor = AutoProcessor.from_pretrained(ckpt)
# image = Image.open(requests.get(url, stream=True).raw)
# prompt = "<start_of_image> in this image, there is"
# model_inputs = processor(text=prompt, images=image, return_tensors="pt")
# input_len = model_inputs["input_ids"].shape[-1]
# with torch.inference_mode():
# generation = model.generate(**model_inputs, max_new_tokens=100, do_sample=False)
# generation = generation[0][input_len:]
@spaces.GPU(duration=duration)
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p,):
# messages = [{"role": "system", "content": system_message}]
messages = [{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/spaces/big-vision/paligemma-hf/resolve/main/examples/password.jpg"},
{"type": "text", "text": "What is the password?"}
]}]
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})
response = ""
# for message in client.chat_completion(messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p,):
# token = message.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
"""
demo = gr.ChatInterface(
respond,
textbox=gr.MultimodalTextbox(),
multimodal=True,
stop_btn="Stop generation",
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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