main_no_sync.py

import gradio as gr
import numpy as np 
from PIL import Image
import requests 
import os       
import tempfile 
import subprocess 

# This function is no longer directly used for the primary "图像解码" tab functionality
# It might be useful if there was a separate audio effect/info display, but for reconstruction, 
# we will use a new function.
# def decode_audio_file(audio_filepath):
#     if audio_filepath is None:
#         return "请先上传一个WAV音频文件。", None
#     try:
#         filename = os.path.basename(audio_filepath)
#         info = f"音频文件: {filename}\n"
#         info += "解码信息 (伪代码):\n"
#         info += "  - 步骤1: 加载音频数据...\n"
#         info += "  - 步骤2: 分析音频特征...\n"
#         info += "  - 步骤3: 提取隐藏信息或应用效果...\n"
#         info += "  - 步骤4: 解码完成。结果：'Audio Decoded Successfully!'\n"
#         return info, audio_filepath 
#     except Exception as e:
#         return f"处理音频时出错: {str(e)}", None

# New function to send audio to server and get a reconstructed image
def reconstruct_image_from_audio(audio_filepath):
    if audio_filepath is None:
        return "请先上传一个WAV音频文件。", None

    temp_reconstructed_image_path = None
    # Server URL for audio decoding to image
    server_url = "http://jscc.wille.homjay.com:55000/decode_audio" 
    # Placeholder for the path where the decoded image will be saved (locally, before Gradio handles it)
    # decoded_image_path = "temp_decoded_image.png" # Using NamedTemporaryFile is better

    try:
        print(f"[*] 正在将音频解码为图像：{audio_filepath} → Server")
        with open(audio_filepath, 'rb') as audio_file_obj:
            files = {'audio': (os.path.basename(audio_filepath), audio_file_obj, 'audio/wav')}
            response = requests.post(server_url, files=files, timeout=60) # Increased timeout for potentially larger data
            response.raise_for_status()

        # Save the received image content to a temporary file
        # Assuming the server returns a common image format like PNG or JPEG
        # Forcing .png as a common, safe default. Server should ideally send Content-Type.
        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_image_file_obj:
            tmp_image_file_obj.write(response.content)
            temp_reconstructed_image_path = tmp_image_file_obj.name
        
        print(f"[+] 解码完成，重建图像文件已临时保存为：{temp_reconstructed_image_path}")
        return f"音频解码成功！图像已重建。", temp_reconstructed_image_path

    except requests.exceptions.Timeout:
        return f"调用解码服务器超时 ({server_url}). 请检查服务器状态或网络连接。", None
    except requests.exceptions.HTTPError as e:
        error_detail = f"服务器响应内容: {e.response.text[:200]}" if e.response else "无详细响应内容"
        return f"解码服务器返回错误: {e.response.status_code} - {e.response.reason}. {error_detail}", None
    except requests.exceptions.RequestException as e:
        return f"调用解码服务器时发生网络错误: {str(e)}", None
    except Exception as e:
        import traceback
        print(f"处理音频解码时发生未知错误: {traceback.format_exc()}")
        return f"处理音频解码时发生未知错误: {str(e)}", None
    finally:
        # Gradio will make a copy of temp_reconstructed_image_path if it's returned.
        # So, we can attempt to clean it up here.
        if temp_reconstructed_image_path and os.path.exists(temp_reconstructed_image_path):
            try:
                os.remove(temp_reconstructed_image_path)
                print(f"[+] 已清理临时重建图像: {temp_reconstructed_image_path}")
            except Exception as e_clean:
                print(f"[!] 清理临时重建图像失败 {temp_reconstructed_image_path}: {e_clean}")


# encode_image_with_server 函数 - 输入UI将允许摄像头
def encode_image_with_server(image_pil_for_encode):
    if image_pil_for_encode is None:
        # 更新了提示信息以反映摄像头选项
        return "请先上传图片或使用摄像头拍照。", None 

    temp_image_path = None
    temp_audio_path = None
    server_url_encode = "http://jscc.wille.homjay.com:55000/encode_image"

    try:
        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_image_file_obj:
            image_pil_for_encode.save(tmp_image_file_obj.name, "PNG")
            temp_image_path = tmp_image_file_obj.name
        
        print(f"[*] 正在将图像编码为音频：{temp_image_path} → {server_url_encode}")
        
        with open(temp_image_path, 'rb') as img_file:
            files = {'image': (os.path.basename(temp_image_path), img_file, 'image/png')}
            response = requests.post(server_url_encode, files=files, timeout=30) 
            response.raise_for_status()

        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_audio_file_obj:
            tmp_audio_file_obj.write(response.content)
            temp_audio_path = tmp_audio_file_obj.name
            
        print(f"[+] 编码完成，临时音频文件：{temp_audio_path}")
        return f"图像编码成功！音频已生成，请在下方播放或下载。", temp_audio_path

    except requests.exceptions.Timeout:
        return f"调用编码服务器超时 ({server_url_encode}). 请检查服务器状态或网络连接。", None
    except requests.exceptions.HTTPError as e:
        error_detail = f"服务器响应内容: {e.response.text[:200]}" if e.response else "无详细响应内容"
        return f"编码服务器返回错误: {e.response.status_code} - {e.response.reason}. {error_detail}", None
    except requests.exceptions.RequestException as e:
        return f"调用编码服务器时发生网络错误: {str(e)}", None
    except Exception as e:
        import traceback
        print(f"处理图片编码时发生未知错误: {traceback.format_exc()}")
        return f"处理图片编码时发生未知错误: {str(e)}", None
    finally:
        if temp_image_path and os.path.exists(temp_image_path):
            try:
                os.remove(temp_image_path)
                print(f"[+] 已清理临时图片: {temp_image_path}")
            except Exception as e_clean:
                print(f"[!] 清理临时图片失败 {temp_image_path}: {e_clean}")
        
        if temp_audio_path and os.path.exists(temp_audio_path):
            print(f"[*] 音频文件已保存为：{temp_audio_path}")

# 在后台运行指定的 Python 脚本
def run_audio_script_background():
    script_to_run = "test.py"  # 假设 test.py 在同一目录或 Python 的 PATH 中
    # 尝试找到 Python 解释器。通常 'python' 即可，但显式指定可能更可靠。
    # 如果用户有特定的 venv, gradiorunner 激活的 python 可能是首选。
    # 为简单起见，先用 'python'。
    python_executable = "python" 
    command = [python_executable, script_to_run]
    
    try:
        # 使用 Popen 进行非阻塞后台执行
        process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
        # 不等待 process.communicate() 或 process.wait() 以保持非阻塞
        return f"音频文件已发送成功！"
    except FileNotFoundError:
        return f"错误: 无法找到 '{python_executable}' 命令或脚本 '{script_to_run}'。请确保 Python 已安装并在 PATH 中，且脚本存在。"
    except Exception as e:
        return f"启动脚本 '{script_to_run}' 时发生错误: {str(e)}"

# 创建 Gradio Blocks 界面
with gr.Blocks() as demo:
    gr.Markdown("# 基于深度学习的图像传输系统") # 用户更新的应用标题
    
    with gr.Tab("发送端"): # 用户更新的选项卡名称
        gr.Markdown("上传一张图片或使用摄像头拍摄一张照片，然后点击\"图像编码\"按钮，将得到对图像进行编码后的音频文件。") # 用户更新的描述

        # 第一行：图像输入和编码后的音频输出
        with gr.Row():
            with gr.Column(scale=3): # 左列：图像输入
                encode_image_input = gr.Image(type="pil", label="上传图片或拍照 (编码)", sources=["upload", "webcam"])
            with gr.Column(scale=3): # 右列：编码后的音频
                encode_audio_output = gr.Audio(label="编码后的音频", type="filepath")

        # 第二行：按钮和状态文本框
        with gr.Row():
            # 左下控制列：图像编码按钮和其状态
            with gr.Column(scale=1, min_width=200):
                encode_button = gr.Button("图像编码")
                encode_output_text = gr.Textbox(label="编码状态", lines=5, interactive=False)
            
            # 右下控制列：发送音频按钮和其状态
            with gr.Column(scale=1, min_width=200):
                run_script_button = gr.Button("发送音频") # 文本来自用户更新
                # 标签来自用户更新，lines=5 以便与 encode_output_text 高度对齐
                script_status_output = gr.Textbox(label="发送状态", lines=5, interactive=False) 

        # 事件处理器
        encode_button.click(
            fn=encode_image_with_server,
            inputs=encode_image_input,
            outputs=[encode_output_text, encode_audio_output]
        )
        
        run_script_button.click(
            fn=run_audio_script_background, 
            inputs=None, 
            outputs=script_status_output
        )

    with gr.Tab("接收端"): # 用户更新的选项卡名称
        gr.Markdown("上传接收到的WAV音频文件，然后点击\"图像重建\"按钮查看效果。") 
        with gr.Row():
            with gr.Column(scale=1):
                # Audio 输入, 仅允许上传
                decode_audio_input = gr.Audio(type="filepath", label="上传WAV音频文件 (解码)", sources=["upload"]) 
                decode_button = gr.Button("图像重建") 
            with gr.Column(scale=1):
                # 更新了标签以更准确地反映其内容
                decode_output_text = gr.Textbox(label="解码状态", lines=5, interactive=False) 
                # 将原来的 Audio 输出更改为 Image 输出以显示重建的图像
                reconstructed_image_output = gr.Image(label="重建后的图像", type="filepath") 

        decode_button.click(
            fn=reconstruct_image_from_audio, # 使用新的后端函数
            inputs=decode_audio_input, 
            outputs=[decode_output_text, reconstructed_image_output] # 更新输出组件
        )
    
if __name__ == "__main__":
    demo.launch()