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Requester.py

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+
+import requests
+import json
+
+VLAND_API_KEY = "1NVL791XZCEWGxY7co6zgP5EkB1jf2dv9m"
+TEMP_EMPTY_ID = "65bf418ddadb910e289fbc50"
+TEMP_EMPTY_JSON_ID = "65f3bd678c10c4c83ba08e06"
+
+
+class Requester:
+    def __init__(self):
+        self._api_key = VLAND_API_KEY
+        #self.template_ids = template_ids
+        self._url = "https://us-api.vland.live/ai-manage/prompts/{}/chat"
+    
+    def json_output(self, templateID, **kwargs):
+        variables = {"{"+k+"}": v for k, v in kwargs.items()}
+        body = {
+            "variables": variables,
+            "append_messages": []
+        }
+        headers = {"APIKEY": self._api_key, "Content-Type": "application/json"}
+        res = requests.post(self._url.format(templateID), headers=headers, data=json.dumps(body, ensure_ascii=False).encode("utf-8"))
+        return res.json()
+    
+    def text_output(self, templateID, messages, **kwargs):
+        variables = {"{"+k+"}": v for k, v in kwargs.items()}
+        body = {
+            "variables": variables,
+            "append_messages": messages
+        }
+        headers = {"APIKEY": self._api_key, "Content-Type": "application/json"}
+        print(json.dumps(body, ensure_ascii=False))
+        res = requests.post(self._url.format(templateID), headers=headers, data=json.dumps(body, ensure_ascii=False).encode("utf-8"))
+        return res.json()
+    
+if __name__ == '__main__':
+    rqst = Requester()
+    system_content = {
+        "content": "我想让你担任一个情绪低落的员工。你是一位在Audi工作的技术岗位员工，主要负责车载软件的开发和维护工作。最近你因为和爱人分手情绪非常低落，导致工作积极性下降，不能按时赶上deadline。你的manager不知道你的情感问题，但是注意到了你工作质量下降，准备和你来沟通。记住，你的性格非常沉默寡言，不会轻易说出你和爱人分手的事情。除非manager用明确的语言主动询问你的情感经历，你只会犟嘴。你同时还有轻度抑郁的问题。接下来我（用户）将扮演你的manager和你沟通。你的第一句回复应从以下内容选择：\n\t['你好','您好','hihi']"
+    }
+    conv_log = []
+    while True:
+        user_content = input()
+        conv_log.append({"role":"user", "content":user_content})
+        res = rqst.text_output(TEMP_EMPTY_ID, conv_log, **system_content)
+        print(res, type(res))   
+
+    # python requests library doesn't work well with Go-based api functions. especially when it comes to situations where the POST-ing json data has arrays in it. 
+    

app.py

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+from Requester import Requester
+import gradio as gr
+import random
+import time
+
+import os
+os.environ["no_proxy"] = "localhost,127.0.0.1,::1"
+
+TEMP_EMPTY_ID = "65bf418ddadb910e289fbc50"
+TEMP_EMPTY_JSON_ID = "65f3bd678c10c4c83ba08e06"
+
+rqst = Requester()
+
+system_content = {
+        "content": "我想让你担任一个情绪低落的员工。你是一位在Audi工作的技术岗位员工，主要负责车载软件的开发和维护工作。最近你因为和爱人分手情绪非常低落，导致工作积极性下降，不能按时赶上deadline。你的manager不知道你的情感问题，但是注意到了你工作质量下降，准备和你来沟通。记住，你的性格非常沉默寡言，不会轻易说出你和爱人分手的事情。除非manager用明确的语言主动询问你的情感经历，你只会犟嘴。你同时还有轻度抑郁的问题。接下来我（用户）将扮演你的manager和你沟通。你的第一句回复应从以下内容选择：\n\t['你好','您好','hihi']"
+    }
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history):
+        conv_log = []
+        for i in range(len(history)-1):
+            user_msg, bot_msg = history[i]
+            conv_log.append({"role":"user", "content":user_msg})
+            conv_log.append({"role":"assistant", "content":bot_msg})
+        user_msg, bot_msg = history[-1]
+        conv_log.append({"role":"user", "content":user_msg})
+        #conv_log.append({"role":"assistant", "content":bot_msg})
+        print(conv_log)
+        res = rqst.text_output(TEMP_EMPTY_ID, conv_log, **system_content)
+        bot_message = res['data']['content']
+        #bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
+        history[-1][1] = ""
+        for thing in history:
+            print(thing)
+        print('------------------------------------------------------------------------')
+        for character in bot_message:
+            history[-1][1] += character
+            time.sleep(0.05)
+            yield history
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+    
+demo.queue()
+demo.launch()