Gradio: 快速构建你的 webApp

pip install -U pip -i https://pypi.tuna.tsinghua.edu.cn/simplepip install gradio  -i https://pypi.tuna.tsinghua.edu.cn/simple

# app.pyimport gradio as gr
def greet(name):    return "Hello " + name + "!"
demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch(server_name="0.0.0.0")
# 启动# python -u app.py# Running on local URL:  http://0.0.0.0:7860# To create a public link, set `share=True` in `launch()`

import gradio as grfrom transformers import DPTFeatureExtractor, DPTForDepthEstimationimport torchimport numpy as npfrom PIL import Imageimport open3d as o3dfrom pathlib import Pathimport os
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
def process_image(image_path):    image_path = Path(image_path)    image_raw = Image.open(image_path)    image = image_raw.resize(        (800, int(800 * image_raw.size[1] / image_raw.size[0])),        Image.Resampling.LANCZOS)
    # prepare image for the model    encoding = feature_extractor(image, return_tensors="pt")
    # forward pass    with torch.no_grad():        outputs = model(**encoding)        predicted_depth = outputs.predicted_depth
    ## ... 省略    return [img, gltf_path, gltf_path]
title = "Demo: zero-shot depth estimation with DPT + 3D Point Cloud"description = "This demo is a variation from the original DPT Demo. It uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object."examples = [["examples/1-jonathan-borba-CgWTqYxHEkg-unsplash.jpg"]]
iface = gr.Interface(fn=process_image,                     inputs=[gr.Image(                         type="filepath", label="Input Image")],                     outputs=[gr.Image(label="predicted depth", type="pil"),                              gr.Model3D(label="3d mesh reconstruction", clear_color=[                                                 1.0, 1.0, 1.0, 1.0]),                              gr.File(label="3d gLTF")],                     title=title,                     description=description,                     examples=examples,                     allow_flagging="never",                     cache_examples=False)
iface.launch(debug=True, enable_queue=False)