Update app.py

app.py

@@ -1,24 +1,16 @@
-from transformers import pipeline
 import gradio as gr
 import numpy as np
 import time
 import json
 import os
-from langchain_openai import ChatOpenAI
-from langchain_core.output_parsers import StrOutputParser
 from dotenv import load_dotenv
 import logging
 
 load_dotenv()
 zhipuai_api_key = os.getenv("ZHIPUAI_API_KEY")
 
-# 使用更小的中文Whisper模型
-try:
-    transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")
-    logging.info("Whisper model loaded successfully")
-except Exception as e:
-    logging.error(f"Error loading Whisper model: {e}")
-    transcriber = None
+# 设置日志记录
+logging.basicConfig(level=logging.INFO)
 
 # 初始化对话记录
 conversation = []
@@ -31,40 +23,22 @@ def transcribe(audio):
         return "No audio input received"
     
     try:
-        logging.info(f"Audio input received: {type(audio)}, {len(audio)}")
-        sr, y = audio
-        logging.info(f"Sample rate: {sr}, Audio data shape: {y.shape}")
+        logging.info(f"Audio input received: {type(audio)}")
         
-        # 转换为单声道
-        if y.ndim > 1:
-            y = y.mean(axis=1)
+        # 简单的音频处理
+        audio_array = audio.flatten()  # 将音频转换为一维数组
+        audio_text = f"Received audio with length: {len(audio_array)}"
         
-        logging.info(f"Audio data shape after conversion: {y.shape}")
-        
-        y = y.astype(np.float32)
-        y /= np.max(np.abs(y))
-
-        # 使用中文进行转录
-        if transcriber is not None:
-            logging.info("Starting transcription")
-            result = transcriber({"sampling_rate": sr, "raw": y}, generate_kwargs={"language": "chinese"})
-            text = result["text"].strip()
-            logging.info(f"Transcription result: {text}")
-        else:
-            logging.error("Transcriber not initialized")
-            return "Transcriber not initialized"
-
         # 创建结构化数据
-        if text:
-            current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
-            conversation.append({
-                "时间": current_time,
-                "角色": current_speaker,
-                "内容": text
-            })
-            
-            # 切换说话者
-            current_speaker = "医生" if current_speaker == "患者" else "患者"
+        current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+        conversation.append({
+            "时间": current_time,
+            "角色": current_speaker,
+            "内容": audio_text
+        })
+        
+        # 切换说话者
+        current_speaker = "医生" if current_speaker == "患者" else "患者"
 
         # 将对话记录转换为格式化的字符串
         formatted_conversation = json.dumps(conversation, ensure_ascii=False, indent=2)
@@ -79,56 +53,20 @@ def switch_speaker():
     current_speaker = "医生" if current_speaker == "患者" else "患者"
     return f"当前说话者：{current_speaker}"
 
-def generate_memo(conversation_json):
-    llm = ChatOpenAI(
-        model="glm-3-turbo",
-        temperature=0.7, 
-        openai_api_key=zhipuai_api_key,
-        openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
-    )
-    
-    prompt = f"""
-    请根据以下医生和患者的对话，生成一份结构化的备忘录。备忘录应包含以下字段：主诉、检查、诊断、治疗和备注。
-    如果某个字段在对话中没有明确提及，请填写"未提及"。
-
-    对话内容：
-    {conversation_json}
-
-    请以JSON格式输出备忘录，格式如下：
-    {{
-        "主诉": "患者的主要症状和不适",
-        "检查": "医生建议或已进行的检查",
-        "诊断": "医生对患者的诊断",
-        "治疗": "医生对患者的治疗建议",
-        "备注": "医生对患者的备注"
-    }}
-    """
-    
-    output = llm.invoke(prompt)
-    output_parser = StrOutputParser()
-    output = output_parser.invoke(output)
-    #st.info(output)
-    return output
-
-
 # 创建Gradio界面
 with gr.Blocks() as demo:
-    gr.Markdown("# 实时中文对话转录与备忘录生成")
-    gr.Markdown("点击麦克风图标开始录音，说话后会自动进行语音识别。支持中文识别。")
+    gr.Markdown("# 音频输入测试")
+    gr.Markdown("上传音频文件或使用麦克风录音。")
     
     with gr.Row():
-        audio_input = gr.Audio(sources=["microphone"], type="numpy", streaming=True)
+        audio_input = gr.Audio(source="microphone", type="numpy")
         speaker_button = gr.Button("切换说话者")
     
     speaker_label = gr.Label("当前说话者：患者")
     conversation_output = gr.JSON(label="对话记录")
-    memo_output = gr.JSON(label="备忘录")
-    
-    generate_memo_button = gr.Button("生成备忘录")
     
-    audio_input.stream(transcribe, inputs=[audio_input], outputs=[conversation_output])
+    audio_input.change(transcribe, inputs=[audio_input], outputs=[conversation_output])
     speaker_button.click(switch_speaker, outputs=[speaker_label])
-    generate_memo_button.click(generate_memo, inputs=[conversation_output], outputs=[memo_output])
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()