app.py

import os
import base64
import gradio as gr
from openai import OpenAI

def encode_image(image_path):
    """将本地图片文件转换为 Base64 编码"""
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

def detect_language(text, history):
    """
    智能检测用户语言：
    1. 优先从当前提问检测
    2. 若当前无文字，从最近一轮用户历史中推断
    3. 默认返回中文
    """
    # 1. 检查当前提问
    if text:
        clean_text = str(text).strip()
        if clean_text:
            # 计算中文字符和英文字母比例
            chinese_chars = sum(1 for char in clean_text if '\u4e00' <= char <= '\u9fff')
            alpha_chars = sum(1 for char in clean_text if char.isalpha())
            
            if alpha_chars > 0:
                # 中文字符占比 > 30% 判定为中文
                if chinese_chars / alpha_chars > 0.3:
                    return "zh"
                else:
                    return "en"
            else:
                # 没有字母（纯数字/符号），默认中文
                return "zh"
    
    # 2. 当前无文字，从历史中推断
    for msg in reversed(history[-6:]):
        if msg.get("role") == "user":
            content = msg.get("content", "")
            if isinstance(content, dict):
                content = content.get("text", "")
            if content:
                clean_content = str(content).strip()
                chinese_chars = sum(1 for char in clean_content if '\u4e00' <= char <= '\u9fff')
                alpha_chars = sum(1 for char in clean_content if char.isalpha())
                if alpha_chars > 0 and chinese_chars / alpha_chars > 0.3:
                    return "zh"
                elif alpha_chars > 0:
                    return "en"
    
    # 3. 默认中文
    return "zh"

def predict(message, history):
    api_key = os.getenv("SILICONFLOW_API_KEY", "")
    if not api_key:
        yield "❌ 未检测到 API Key！请先在 Space Settings -> Secrets 中添加 SILICONFLOW_API_KEY！"
        return

    api_key = api_key.strip()

    try:
        client = OpenAI(
            api_key=api_key,
            base_url="https://api.siliconflow.cn/v1"
        )

        text_content = ""
        files = []

        # Gradio 多模态输入解析
        if isinstance(message, dict):
            text_content = message.get("text", "")
            files = message.get("files", [])
        else:
            text_content = str(message)

        # 智能检测语言（含历史记忆）
        user_lang = detect_language(text_content, history)
        
        # 🔥 调试输出（可删除）
        print(f"检测到语言: {user_lang}, 用户输入: {text_content[:50]}")

        # 构建当前请求的 Message Payload
        current_content = []

        # 1. 解析当前上传的图片
        for f in files:
            file_path = f.get("path") if isinstance(f, dict) else f
            if file_path:
                try:
                    base64_img = encode_image(file_path)
                    current_content.append({
                        "type": "image_url",
                        "image_url": {"url": f"data:image/jpeg;base64,{base64_img}"}
                    })
                except Exception as img_err:
                    print(f"图片读取失败: {img_err}")

        # 2. 解析文本说明
        if not text_content or not str(text_content).strip():
            if user_lang == "en":
                prompt_text = "Please observe this image carefully and tell me what plant this is and its key features."
            else:
                prompt_text = "请仔细观察这张图片，告诉我这是什么植物？有哪些特征？"
        else:
            prompt_text = str(text_content).strip()

        current_content.append({"type": "text", "text": prompt_text})

        # 3. 根据用户语言设置 System Prompt
        if user_lang == "en":
            system_prompt = (
                "You are a professional botanist and nature expert. "
                "Identify the plant in the image and provide its scientific name, "
                "family, genus, and key features in **English**. "
                "Be concise and accurate."
            )
        else:
            system_prompt = (
                "你是一位专业的植物学家和自然科普专家。"
                "请精准识别图片中的植物，并用简洁明了的中文回答其名称、科属及特点。"
            )

        messages = [
            {"role": "system", "content": system_prompt}
        ]

        # 仅保留最近 2 轮纯文本历史
        for msg in history[-4:]:
            role = "user" if msg.get("role") == "user" else "assistant"
            content_val = msg.get("content", "")
            if isinstance(content_val, dict):
                content_val = content_val.get("text", "")
            if content_val and not str(content_val).startswith("data:image"):
                messages.append({"role": role, "content": str(content_val)})

        # 放入最新一轮包含图片的请求
        messages.append({"role": "user", "content": current_content})

        # 4. 调用视觉模型
        response = client.chat.completions.create(
            model="Qwen/Qwen3-VL-32B-Instruct",
            messages=messages,
            stream=True,
            temperature=0.2
        )

        # 5. 流式输出
        partial_text = ""
        for chunk in response:
            if chunk.choices and chunk.choices[0].delta.content:
                partial_text += chunk.choices[0].delta.content
                yield partial_text

    except Exception as e:
        err_msg = str(e)
        if "30001" in err_msg or "insufficient" in err_msg.lower():
            yield "⚠️ **请求失败：硅基流动账号余额不足**"
        elif "30003" in err_msg or "Model disabled" in err_msg:
            yield "⚠️ **模型不可用，请检查模型名称是否正确**"
        else:
            yield f"❌ 请求失败: {err_msg}"

# 创建 Gradio 界面
demo = gr.ChatInterface(
    fn=predict,
    title="🌱 AI 植物识别专家 (智能双语)",
    description="🌍 智能识别你的提问语言：中文问→中文答，英文问→英文答。连续对话自动记忆语言偏好！",
    multimodal=True
)

if __name__ == "__main__":
    demo.launch(ssr_mode=False)