app.py

import gradio as gr
import time  # 模拟处理耗时


nlp = spacy.load("en_core_web_md")
def process_api(input_text):
    # 这里编写实际的后端处理逻辑

    return {
        "status": "success",
        "result": f"Processed: {input_text.upper()}",
        "timestamp": time.time()
    }

# 设置API格式为JSON
gr.Interface(
    fn=process_api,
    inputs="text",
    outputs="json",
    title="Backend API",
    allow_flagging="never"
).launch()


# import gradio as gr
# import spacy
# from spacy import displacy
# import pandas as pd
# import time

# nlp = spacy.load("en_core_web_md")
# HTML_WRAPPER = "<div style='padding: 10px;'>{}</div>"

# def show_spatial_ent_table(doc):
#     rows = []
#     for i, ent in enumerate(doc.ents):
#         rows.append(f"<tr><td>{i+1}</td><td>{ent.text}</td><td>{ent.label_}</td></tr>")
#     table_html = "<table border='1'><tr><th>Index</th><th>Entity</th><th>Label</th></tr>" + "".join(rows) + "</table>"
#     return table_html

# def process_api(input_text):
#     doc = nlp(input_text)
#     html_ent = displacy.render(doc, style="ent")
#     html_ent = HTML_WRAPPER.format(html_ent.replace("\n", ""))
#     html_table = show_spatial_ent_table(doc)
#     final_html = html_ent + "<br>" + html_table
#     return {
#         "data": [{"html": final_html}],
#         "timestamp": time.time()
#     }

# gr.Interface(
#     fn=process_api,
#     inputs="text",
#     outputs="json",
#     allow_flagging="never",
#     title="Backend API"
# ).launch()