move scripts/upload_openxlab.py and scripts/trans_process.py

generate_data/config.yml

@@ -1,11 +1,11 @@
-aistudio _token : '{your_token}' # 文心一言的token
+aistudio_token : '{your_token}'      # 文心一言的token
-dashscope_api_key : '{your_api_key}' #通义千问的api_key
+dashscope_api_key : '{your_api_key}' # 通义千问的api_key
-zhiouai_api_key : '{your_api_key}' # 智浦AI的密钥
+zhiouai_api_key : '{your_api_key}'   # 智谱AI的密钥
 
 # 星火大模型配置
-appid : "{}"  # 填写控制台中获取的 APPID 信息
+appid : "{}"       # 填写控制台中获取的 APPID 信息
 api_secret : "{}"  # 填写控制台中获取的 APISecret 信息
-api_key : "{}"  # 填写控制台中获取的 APIKey 信息
+api_key : "{}"     # 填写控制台中获取的 APIKey 信息
 
 
 system : '现在你是一个心理专家，我有一些心理问题，请你用专业的知识帮我解决'

generate_data/requirements.txt

@@ -1,5 +1,5 @@
-erniebot  #文心一言
+erniebot      # 文心一言
-dashscope  # 通义千问
+dashscope     # 通义千问
-zhipuai # 智浦
+zhipuai       # 智谱
-python-dotenv # 智浦
+python-dotenv # 智谱
-websocket #调用星火大模型的时候会使用 
+websocket     # 调用星火大模型的时候会使用 

generate_data/trans_process.py

@@ -0,0 +1,78 @@
+import json
+from tqdm import tqdm
+
+
+def qwen_api(prompt):
+    import dashscope
+    from http import HTTPStatus
+
+    dashscope.api_key = "your key"
+    prompt = "你是一位非常擅长将英文翻译成中文的专家。请你将下面的英文翻译成正确地道的中文，要求只返回翻译的中文句子:\n" + prompt
+    response = dashscope.Generation.call(
+        model='qwen-max',
+        prompt=prompt,
+        history=[],
+    )
+
+    if response.status_code == HTTPStatus.OK:
+        result = response.output.text
+        # print(result)
+    else:
+        result = 'ERROR'
+    return result
+
+
+def get_conversation_list():
+    with open('./ESConv.json', 'rt', encoding='utf-8') as file:
+        data = json.load(file)
+
+    idx = 0
+    conversation_list = []
+    for itm in tqdm(data):
+        one_conversation = {
+            "conversation": []
+        }
+        dia_tuple = []
+        for dia in tqdm(itm['dialog']):
+            # print(dia['speaker'], dia['content'])
+            if dia['speaker'] == 'seeker':
+                dia_tuple.append(qwen_api(dia['content']))
+            elif dia['speaker'] == 'supporter':
+                dia_tuple.append(qwen_api(dia['content']))
+            else:
+                exit("不存在角色!")
+
+            if len(dia_tuple) == 2 and len(one_conversation['conversation']) == 0:
+                one_conversation['conversation'].append(
+                    {
+                        "system": "现在你是一个心理专家，我有一些心理问题，请你用专业的知识帮我解决。",
+                        "input": dia_tuple[0],
+                        "output": dia_tuple[1]
+                    },
+                )
+                dia_tuple = []
+
+            elif len(dia_tuple) == 2:
+                one_conversation['conversation'].append(
+                    {
+                        "input": dia_tuple[0],
+                        "output": dia_tuple[1]
+                    },
+                )
+                dia_tuple = []
+
+        conversation_list.append(one_conversation)
+        idx += 1
+
+        # if (idx == 1):
+        #     print(conversation_list)
+        #     break
+        print(idx)
+    return conversation_list
+
+
+if __name__ == '__main__':
+    conversation_list = get_conversation_list()
+    # 将conversation_list保存为一个json文件
+    with open('conversation_list.json', 'wt', encoding='utf-8') as f:
+        json.dump(conversation_list, f, ensure_ascii=False)

generate_data/upload_openxlab.py

@@ -0,0 +1,3 @@
+import os
+
+os.system("openxlab model create --model-repo='jujimeizuo/EmoLLM_Model' -s ./metafile.yml")

generate_data/zhipuai_gen_data.py

@@ -34,11 +34,21 @@ def zhipu_api(data, emo):
     
     top_p = round(random.uniform(0.1, 0.9), 2)
     messages = getText('user', prompt)
-    response = client.chat.completions.create(
+    
-        model='glm-4',
+    # Error code: 400, with error text {"error":{"code":"1301","message":
-        messages=messages,
+    # "系统检测到输入或生成内容可能包含不安全或敏感内容，请您避免输入易产生敏感内容的提示语，感谢您的配合。"}}
-        top_p=top_p,
+    try:
-    )
+        response = client.chat.completions.create(
+            model='glm-4',
+            messages=messages,
+            top_p=top_p,
+        )
+    except:
+        response = client.chat.completions.create(
+            model='glm-4',
+            messages=messages,
+            top_p=top_p,
+        )
 
     return response.choices[0].message.content