import gradio as gr
from fastapi import FastAPI, Request
import uvicorn
import os
from huggingface_hub import InferenceClient
import logging
import traceback

# 设置日志
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# ===== 测试代码开始 =====
print("="*50)
print("🔍 开始测试模型调用")
print("="*50)

HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
    print("❌ 错误: HF_TOKEN 环境变量未设置!")
else:
    print(f"✅ HF_TOKEN 已设置 (长度: {len(HF_TOKEN)})")
    if HF_TOKEN.startswith("hf_"):
        print("✅ HF_TOKEN 格式正确")
    else:
        print("⚠️ 警告: HF_TOKEN 格式可能不正确，应以 hf_ 开头")

# 测试你的模型
model_id = "jiang1002/chatglm-6b-adgen"
print(f"\n📊 正在测试模型 '{model_id}'...")

# 方法1：测试 Hugging Face 免费推理（不指定 provider）
try:
    print("\n🔄 测试1: 使用 Hugging Face 免费推理...")
    client1 = InferenceClient(token=HF_TOKEN)
    response1 = client1.text_generation(
        "你好",
        model=model_id,
        max_new_tokens=20
    )
    print(f"✅ 免费推理成功! 响应: {response1[:50]}...")
except Exception as e:
    print(f"❌ 免费推理失败: {str(e)}")

# 方法2：测试 auto provider（自动选择）
try:
    print("\n🔄 测试2: 使用 auto provider...")
    client2 = InferenceClient(provider="auto", token=HF_TOKEN)
    response2 = client2.chat.completions.create(
        model=model_id,
        messages=[{"role": "user", "content": "你好"}],
        max_tokens=20
    )
    print(f"✅ auto provider 成功! 响应: {response2.choices[0].message.content[:50]}...")
except Exception as e:
    print(f"❌ auto provider 失败: {str(e)}")

# 方法3：测试 Groq（如果配置了）
try:
    print("\n🔄 测试3: 使用 Groq...")
    client3 = InferenceClient(provider="groq", token=HF_TOKEN)
    response3 = client3.chat.completions.create(
        model=model_id,
        messages=[{"role": "user", "content": "你好"}],
        max_tokens=20
    )
    print(f"✅ Groq 成功! 响应: {response3.choices[0].message.content[:50]}...")
except Exception as e:
    print(f"❌ Groq 失败: {str(e)}")

# 方法4：测试 Together AI（如果配置了）
try:
    print("\n🔄 测试4: 使用 Together AI...")
    client4 = InferenceClient(provider="together-ai", token=HF_TOKEN)
    response4 = client4.chat.completions.create(
        model=model_id,
        messages=[{"role": "user", "content": "你好"}],
        max_tokens=20
    )
    print(f"✅ Together AI 成功! 响应: {response4.choices[0].message.content[:50]}...")
except Exception as e:
    print(f"❌ Together AI 失败: {str(e)}")

print("\n" + "="*50)
print("🔍 测试结束，继续启动应用...")
print("="*50)
# ===== 测试代码结束 =====

# 初始化 FastAPI
app = FastAPI()

# 从环境变量获取 Hugging Face Token
HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
    logger.warning("⚠️ 未设置 HF_TOKEN 环境变量，API 调用可能失败")

# 初始化 InferenceClient
# 这里用 provider="auto" 让系统自动选择可用提供商
client = InferenceClient(provider="auto", token=HF_TOKEN)

# 你的模型名称
MODEL_ID = "jiang1002/chatglm-6b-adgen"  # 或者换成其他公开模型

# --- 1. API 接口 ---
@app.post("/generate")
async def generate(request: Request):
    try:
        data = await request.json()
        prompt = data.get("text", "")
        messages = data.get("messages", [])
        
        # 如果提供了完整的 messages 格式，就用它
        if messages:
            response = client.chat.completions.create(
                model=MODEL_ID,
                messages=messages
            )
            result = response.choices[0].message.content
        else:
            # 否则用简单的 prompt 格式
            response = client.text_generation(
                prompt,
                model=MODEL_ID,
                max_new_tokens=512,
                temperature=0.7
            )
            result = response
            
        return {"success": True, "result": result}
    except Exception as e:
        logger.error(f"API 调用失败: {str(e)}")
        return {"success": False, "error": str(e)}

# --- 2. Gradio 聊天界面 ---
def chat_func(message, history):
    """Gradio 聊天函数"""
    try:
        # 将历史记录转换为 messages 格式
        messages = []
        for human, assistant in history:
            messages.append({"role": "user", "content": human})
            messages.append({"role": "assistant", "content": assistant})
        messages.append({"role": "user", "content": message})
        
        # 调用 Inference API
        response = client.chat.completions.create(
            model=MODEL_ID,
            messages=messages,
            max_tokens=512,
            temperature=0.7
        )
        
        return response.choices[0].message.content
    except Exception as e:
        logger.error(f"聊天失败: {str(e)}")
        logger.error(f"详细错误: {traceback.format_exc()}")  # 添加这行
        return f"调用失败: {str(e)}\n\n{traceback.format_exc()}"

# 创建 Gradio 界面
demo = gr.ChatInterface(
    fn=chat_func,
    title="ChatGLM 广告生成助手 (使用 Inference Providers)",
    description="后台使用 Hugging Face Inference Providers，无需本地 GPU"
)

# 挂载 Gradio
app = gr.mount_gradio_app(app, demo, path="/")

# 添加健康检查端点
@app.get("/health")
async def health():
    return {"status": "ok", "model": MODEL_ID}

if __name__ == "__main__":
    port = int(os.getenv("PORT", 7860))
    uvicorn.run(app, host="0.0.0.0", port=port)