Update app.py

app.py

@@ -1,46 +1,66 @@
-import streamlit as st
-import requests
-import os
-
-# ✅ 从 Hugging Face Secrets 中读取 API Token
-API_TOKEN = os.getenv("HF_API_TOKEN")
-
-# ✅ 设置后端 API 地址
-# BACKEND_URL = "https://dsbb0707-SpatialParsebackcopy.hf.space/api/predict/"
-BACKEND_URL = "https://dsbb0707--dockerb2.hf.space/predict"
-def call_backend(input_text):
-    try:
-        headers = {
-            "Authorization": f"Bearer {API_TOKEN}"
-        }
-        response = requests.post(
-            BACKEND_URL,
-            headers=headers,
-            json={"data": [input_text]},
-            timeout=10
-        )
-        if response.status_code == 200:
-            result = response.json()["data"][0]
-            return f"✅ {result['result']}\n⏰ {result['timestamp']}"
-        return f"❌ Backend Error (HTTP {response.status_code})"
-    except Exception as e:
-        return f"⚠️ Connection Error: {str(e)}"
-
-# ✅ Streamlit UI 界面
-st.set_page_config(page_title="空间信息前端", page_icon="🧠")
-st.title("🌏 空间解析前端")
-st.markdown("通过 Hugging Face API 与后端交互")
-
-# ✅ 用户输入
-user_input = st.text_input("请输入文本", "")
-
-# ✅ 提交按钮
-if st.button("提交"):
-    if not user_input.strip():
-        st.warning("请先输入文本。")
-    else:
-        with st.spinner("🔄 正在调用后端..."):
-            result = call_backend(user_input)
-        st.success("完成！")
-        st.text_area("后端返回结果", result, height=150)
+# import streamlit as st
+# import requests
+# import os
 
+# # ✅ 从 Hugging Face Secrets 中读取 API Token
+# API_TOKEN = os.getenv("HF_API_TOKEN")
+
+# # ✅ 设置后端 API 地址
+# # BACKEND_URL = "https://dsbb0707-SpatialParsebackcopy.hf.space/api/predict/"
+# BACKEND_URL = "https://dsbb0707--dockerb2.hf.space/predict"
+# def call_backend(input_text):
+#     try:
+#         headers = {
+#             "Authorization": f"Bearer {API_TOKEN}"
+#         }
+#         response = requests.post(
+#             BACKEND_URL,
+#             headers=headers,
+#             json={"data": [input_text]},
+#             timeout=10
+#         )
+#         if response.status_code == 200:
+#             result = response.json()["data"][0]
+#             return f"✅ {result['result']}\n⏰ {result['timestamp']}"
+#         return f"❌ Backend Error (HTTP {response.status_code})"
+#     except Exception as e:
+#         return f"⚠️ Connection Error: {str(e)}"
+
+# # ✅ Streamlit UI 界面
+# st.set_page_config(page_title="空间信息前端", page_icon="🧠")
+# st.title("🌏 空间解析前端")
+# st.markdown("通过 Hugging Face API 与后端交互")
+
+# # ✅ 用户输入
+# user_input = st.text_input("请输入文本", "")
+
+# # ✅ 提交按钮
+# if st.button("提交"):
+#     if not user_input.strip():
+#         st.warning("请先输入文本。")
+#     else:
+#         with st.spinner("🔄 正在调用后端..."):
+#             result = call_backend(user_input)
+#         st.success("完成！")
+#         st.text_area("后端返回结果", result, height=150)
+
+import gradio as gr
+import time  # 模拟处理耗时
+
+def process_api(input_text):
+    # 这里编写实际的后端处理逻辑
+    time.sleep(1)  # 模拟处理延迟
+    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()