First Commit for test Hugging face platform

app.py

@@ -1,27 +1,47 @@
-import streamlit as st
 import requests
+import streamlit as st
+from flask import Flask, request, jsonify
+import threading
+
+# Flask 部分
+flask_app = Flask(__name__)
 
-# Streamlit 页面设置
-st.title('AI Model API Interface')
-st.write('输入文本，获取模型预测结果。')
+@flask_app.route('/predict', methods=['POST'])
+def predict():
+    data = request.get_json()
+    if not data or 'input' not in data:
+        return jsonify({'error': 'Invalid input'}), 400
+    input_text = data['input']
+    # 模拟模型预测
+    result = {"prediction": f"Predicted for: {input_text}"}
+    return jsonify(result)
+
+def run_flask():
+    flask_app.run(port=5000, debug=False)
+
+# Streamlit 部分
+st.title("AI Model API Interface")
+st.write("输入文本，获取模型预测结果。")
 
 # 用户输入
-user_input = st.text_area('请输入文本:')
+user_input = st.text_area("请输入文本:")
 
-if st.button('提交'):
+if st.button("提交"):
     if user_input.strip():
-        with st.spinner('正在预测...'):
+        with st.spinner("正在预测..."):
             try:
-                response = requests.post(
-                    'http://127.0.0.1:5000/predict',
-                    json={'input': user_input}
-                )
+                api_url = "http://127.0.0.1:5000/predict"  # 内部调用 Flask
+                response = requests.post(api_url, json={"input": user_input})
                 if response.status_code == 200:
                     prediction = response.json()
-                    st.success(f'预测结果: {prediction}')
+                    st.success(f"预测结果: {prediction}")
                 else:
-                    st.error(f'错误: {response.text}')
+                    st.error(f"错误: {response.text}")
             except Exception as e:
-                st.error(f'请求失败: {e}')
+                st.error(f"请求失败: {e}")
     else:
-        st.warning('请输入文本！')
+        st.warning("请输入文本！")
+
+# 使用线程运行 Flask
+if __name__ == "__main__":
+    threading.Thread(target=run_flask).start()