Update app

app.py

@@ -1,10 +1,96 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+#from huggingface_hub import InferenceClient
 
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
-from new_chat import Conversation, ChatgptAPI
+#from new_chat import Conversation, ChatgptAPI
+
+
+from pathlib import Path
+from openai import OpenAI
+
+class ChatgptAPI:
+    def __init__(self, ):
+        self.client = OpenAI(
+            api_key = "sk-u8YI0ArRHFRRdMEdboouRAXVc3PpR6EhZOfxO4tST5Ua9147",
+            base_url = "https://api.moonshot.cn/v1",
+        )
+    def get_single_round_completion(self, file_path, prompt, conversation):
+        file_object = client.files.create(file=Path(file_path), purpose="file-extract")
+        file_content = client.files.content(file_id=file_object.id).text
+        messages = [
+            {
+                "role": "system",
+                "content": "你是 Kimi，由 Moonshot AI 提供的人工智能助手，你更擅长中文和英文的对话。你会为用户提供安全，有帮助，准确的回答。同时，你会拒绝一切涉及恐怖主义，种族歧视，黄色暴力等问题的回答。Moonshot AI 为专有名词，不可翻译成其他语言。",
+            },
+            {
+                "role": "system",
+                "content": file_content,
+            },
+            {"role": "user", "content": prompt},
+        ]
+        completion = self.client.chat.completions.create(
+            model="moonshot-v1-32k",
+            messages=messages,
+            temperature=0.3,
+        )
+        return completion.choices[0].message
+        
+    
+    def get_multi_round_completion(self, prompt, conversation, model='gpt-3.5-turbo'):
+        conversation.append_question(prompt)
+        prompts = conversation.get_prompts()
+
+        response = openai.ChatCompletion.create(
+            model=model,
+            messages=prompts,
+            temperature=0,
+            max_tokens=2048,
+            top_p=1,
+        )
+        message = response.choices[0].message['content']
+        conversation.append_answer(message)
+
+        return message, conversation
+
+class Conversation:
+    def __init__(self, system_prompt='', num_of_round = 5):
+        self.num_of_round = num_of_round
+        self.history = []
+        self.initialized = False
+        self.history.append({"role": "system", "content": system_prompt})
+
+        if len(system_prompt) > 0:
+            logger.info(f'Conversation initialized with system prompt: {system_prompt}')
+            self.initialized = True
+
+    def is_initialized(self):
+        return self.initialized
+    
+    def append_question(self, question):
+        self.history.append({"role": "user", "content": question})
+
+    def append_answer(self, answer):
+        self.history.append({"role": "assistant", "content": answer})
+
+        if len(self.history) > self.num_of_round * 2:
+            del self.history[1:3]
+
+    def clear(self):
+        self.history.clear()
+        self.initialized = False
+
+    def get_prompts(self):
+        return self.history
+    
+    def round_size(self):
+        return 0 if len(self.history) < 2 else len(self.hitory) - 1
+    
+    def get_history_messages(self):
+        return [(u['content'], b['content']) for u,b in zip(self.history[1::2], self.history[2::2])]
+
+
 
 chat_api = ChatgptAPI()