Create app.py

app.py

@@ -0,0 +1,247 @@
+from flask import Flask, request, Response, jsonify, stream_with_context
+import requests
+import json
+import uuid
+import time
+from datetime import datetime
+
+ORIGINAL_API_URL = "https://app.unlimitedai.chat/api/chat"
+
+app = Flask(__name__)
+
+
+@app.route('/v1/models', methods=['GET'])
+def list_models():
+    # 你可以根据实际情况自定义模型列表
+    models = [
+        {
+            "id": "chat-model-reasoning",
+            "object": "model",
+            "created": 1713235200,
+            "owned_by": "organization-owner",
+            "permission": [],
+            "root": "chat-model-reasoning",
+            "parent": None
+        }
+    ]
+    return jsonify({"object": "list", "data": models})
+
+
+@app.route('/v1/chat/completions', methods=['POST'])
+def chat_completions():
+    data = request.json
+    is_stream = data.get('stream', False)
+    messages = data.get('messages', [])
+    original_messages = []
+    for msg in messages:
+        original_msg = {
+            "id": str(uuid.uuid4()),
+            "createdAt": datetime.utcnow().isoformat() + "Z",
+            "role": msg["role"],
+            "content": msg["content"],
+            "parts": [
+                {
+                    "type": "text",
+                    "text": msg["content"]
+                }
+            ]
+        }
+        original_messages.append(original_msg)
+    original_request = {
+        "id": str(uuid.uuid4()),
+        "messages": original_messages,
+        "selectedChatModel": "chat-model-reasoning"
+    }
+    headers = {'Content-Type': 'application/json'}
+    if is_stream:
+        return stream_response(original_request, headers, data)
+    else:
+        return non_stream_response(original_request, headers, data)
+
+
+def stream_response(original_request, headers, openai_request):
+    def generate():
+        response = requests.post(
+            ORIGINAL_API_URL,
+            headers=headers,
+            json=original_request,
+            stream=True
+        )
+
+        # 用于存储推理和回复内容
+        reasoning_content = ""
+        reply_content = ""
+
+        message_id = None
+
+        for line in response.iter_lines():
+            if not line:
+                continue
+
+            line_str = line.decode('utf-8')
+
+            # 解析不同类型的响应行
+            if line_str.startswith('f:'):
+                # 消息 ID
+                message_data = json.loads(line_str[2:])
+                message_id = message_data.get("messageId")
+
+                # 发送 OpenAI 兼容的流式开始标记
+                start_chunk = {
+                    "id": f"chatcmpl-{uuid.uuid4()}",
+                    "object": "chat.completion.chunk",
+                    "created": int(time.time()),
+                    "model": openai_request.get("model", "gpt-3.5-turbo"),
+                    "choices": [
+                        {
+                            "index": 0,
+                            "delta": {"role": "assistant"},
+                            "finish_reason": None
+                        }
+                    ]
+                }
+                yield f"data: {json.dumps(start_chunk)}\n\n"
+
+            elif line_str.startswith('g:'):
+                # 推理部分，在 OpenAI 格式中不直接显示，但我们可以收集它
+                reasoning_part = line_str[2:].strip('"').replace("\\n", "\n")
+                reasoning_content += reasoning_part
+
+                content_chunk = {
+                    "id": f"chatcmpl-{uuid.uuid4()}",
+                    "object": "chat.completion.chunk",
+                    "created": int(time.time()),
+                    "model": openai_request.get("model", "gpt-3.5-turbo"),
+                    "choices": [
+                        {
+                            "index": 0,
+                            "delta": {"reasoning_content": reasoning_part},
+                            "finish_reason": None
+                        }
+                    ]
+                }
+                yield f"data: {json.dumps(content_chunk)}\n\n"
+
+            elif line_str.startswith('0:'):
+                # 回复部分，这是我们需要流式传输的主要内容
+                reply_part = line_str[2:].strip('"').replace("\\n", "\n")
+                reply_content += reply_part
+
+                # 发送 OpenAI 兼容的内容块
+                content_chunk = {
+                    "id": f"chatcmpl-{uuid.uuid4()}",
+                    "object": "chat.completion.chunk",
+                    "created": int(time.time()),
+                    "model": openai_request.get("model", "gpt-3.5-turbo"),
+                    "choices": [
+                        {
+                            "index": 0,
+                            "delta": {"content": reply_part},
+                            "finish_reason": None
+                        }
+                    ]
+                }
+                yield f"data: {json.dumps(content_chunk)}\n\n"
+
+            elif line_str.startswith('e:') or line_str.startswith('d:'):
+                # 结束标记
+                finish_data = json.loads(line_str[2:])
+                finish_reason = finish_data.get("finishReason", "stop")
+
+                # 发送 OpenAI 兼容的结束块
+                end_chunk = {
+                    "id": f"chatcmpl-{uuid.uuid4()}",
+                    "object": "chat.completion.chunk",
+                    "created": int(time.time()),
+                    "model": openai_request.get("model", "gpt-3.5-turbo"),
+                    "choices": [
+                        {
+                            "index": 0,
+                            "delta": {},
+                            "finish_reason": finish_reason
+                        }
+                    ]
+                }
+                yield f"data: {json.dumps(end_chunk)}\n\n"
+                yield "data: [DONE]\n\n"
+                break
+
+    return Response(
+        stream_with_context(generate()),
+        content_type='text/event-stream'
+    )
+
+
+def non_stream_response(original_request, headers, openai_request):
+    response = requests.post(
+        ORIGINAL_API_URL,
+        headers=headers,
+        json=original_request,
+        stream=True
+    )
+
+    # 用于存储推理和回复内容
+    reasoning_content = ""
+    reply_content = ""
+
+    message_id = None
+    finish_reason = "stop"
+
+    for line in response.iter_lines():
+        if not line:
+            continue
+
+        line_str = line.decode('utf-8')
+
+        # 解析不同类型的响应行
+        if line_str.startswith('f:'):
+            # 消息 ID
+            message_data = json.loads(line_str[2:])
+            message_id = message_data.get("messageId")
+
+        elif line_str.startswith('g:'):
+            # 推理部分
+            reasoning_part = line_str[2:].strip('"')
+            reasoning_content += reasoning_part
+
+        elif line_str.startswith('0:'):
+            # 回复部分
+            reply_part = line_str[2:].strip('"').replace("\\n", "\n")
+            reply_content += reply_part
+
+        elif line_str.startswith('e:') or line_str.startswith('d:'):
+            # 结束标记
+            finish_data = json.loads(line_str[2:])
+            finish_reason = finish_data.get("finishReason", "stop")
+
+    # 构建 OpenAI 兼容的响应
+    openai_response = {
+        "id": f"chatcmpl-{uuid.uuid4()}",
+        "object": "chat.completion",
+        "created": int(time.time()),
+        "model": openai_request.get("model", "gpt-3.5-turbo"),
+        "choices": [
+            {
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": reply_content
+                },
+                "finish_reason": finish_reason
+            }
+        ],
+        "usage": {
+            "prompt_tokens": 0,  # 这里可以根据实际情况设置
+            "completion_tokens": 0,
+            "total_tokens": 0
+        }
+    }
+
+    return jsonify(openai_response)
+
+
+import os
+
+if __name__ == '__main__':
+    port = int(os.environ.get("PORT", 7860))  # 7860 default untuk Hugging Face
+    app.run(host='0.0.0.0', port=port)