File size: 2,101 Bytes
00ab1fc 5bf3ded 00ab1fc 5bf3ded 00ab1fc 5bf3ded 00ab1fc 5bf3ded 00ab1fc 5bf3ded 00ab1fc 5bf3ded 00ab1fc 5bf3ded 00ab1fc 5bf3ded 00ab1fc 5bf3ded 00ab1fc 5bf3ded 00ab1fc 5bf3ded 00ab1fc 5bf3ded 00ab1fc |
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 |
import gradio as gr
from huggingface_hub import InferenceClient
import base64
# 更新为 MiniCPM-Llama3-V-2_5 模型
client = InferenceClient("openbmb/MiniCPM-Llama3-V-2_5")
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
def respond(
message,
image,
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]})
# 处理图片输入
if image:
base64_image = encode_image(image.name)
image_message = f"<image>{base64_image}</image>"
message = image_message + "\n" + message
messages.append({"role": "user", "content": message})
response = ""
for message in client.text_generation(
prompt=f"{messages}",
max_new_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.token.text
response += token
yield response
demo = gr.Interface(
respond,
inputs=[
gr.Textbox(label="Message"),
gr.Image(type="filepath", label="Upload Image"),
gr.State([]), # for history
gr.Textbox(value="You are a friendly AI assistant capable of understanding images and text.", 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)"),
],
outputs=gr.Textbox(label="Response"),
title="MiniCPM-Llama3-V-2_5 Image and Text Chat",
description="Upload an image and ask questions about it, or just chat without an image."
)
if __name__ == "__main__":
demo.launch() |