Update app.py

app.py

@@ -1,66 +1,66 @@
-# import streamlit as st
-# import requests
-# import os
+import streamlit as st
+import requests
+import os
 
-# # ✅ 从 Hugging Face Secrets 中读取 API Token
-# API_TOKEN = os.getenv("HF_API_TOKEN")
+# ✅ 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)}"
+# ✅ 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")
+# ✅ 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", "")
+# ✅ 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)
+# ✅ 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
+# 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()
-    }
+# 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()
+# # 设置API格式为JSON
+# gr.Interface(
+#     fn=process_api,
+#     inputs="text",
+#     outputs="json",
+#     title="Backend API",
+#     allow_flagging="never"
+# ).launch()