chatbot.py

import gradio as gr
import json
import requests

class Chatbot:
    def __init__(self, config):
        self.video_id = config.get('video_id')
        self.content_subject = config.get('content_subject')
        self.content_grade = config.get('content_grade')
        self.jutor_chat_key = config.get('jutor_chat_key')
        self.transcript_text = self.get_transcript_text(config.get('transcript'))
        self.key_moments_text = self.get_key_moments_text(config.get('key_moments'))
        self.ai_model_name = config.get('ai_model_name')
        self.ai_client = config.get('ai_client')
        self.instructions = config.get('instructions')

    def get_transcript_text(self, transcript_data):
        if isinstance(transcript_data, str):
            transcript_json = json.loads(transcript_data)
        else:
            transcript_json = transcript_data
        for entry in transcript_json:
            entry.pop('end_time', None)
        transcript_text = json.dumps(transcript_json, ensure_ascii=False)
        return transcript_text
    
    def get_key_moments_text(self, key_moments_data):
        if isinstance(key_moments_data, str):
            key_moments_json = json.loads(key_moments_data)
        else:
            key_moments_json = key_moments_data
        # key_moments_json remove images
        for moment in key_moments_json:
            moment.pop('images', None)
            moment.pop('end', None)
            moment.pop('transcript', None)
            
        key_moments_text = json.dumps(key_moments_json, ensure_ascii=False)
        return key_moments_text


    def chat(self, user_message, chat_history):
        try:
            messages = self.prepare_messages(chat_history, user_message)
            system_prompt = self.instructions
            service_type = self.ai_model_name
            response_text = self.chat_with_service(service_type, system_prompt, messages)
        except Exception as e:
            print(f"Error: {e}")
            response_text = "學習精靈有點累，請稍後再試！"

        return response_text

    def prepare_messages(self, chat_history, user_message):
        messages = []
        if chat_history is not None:
            if len(chat_history) > 10:
                chat_history = chat_history[-10:]

            for user_msg, assistant_msg in chat_history:
                if user_msg:
                    messages.append({"role": "user", "content": user_msg})
                if assistant_msg:
                    messages.append({"role": "assistant", "content": assistant_msg})
                
        if user_message:
            user_message += "/n (請一定要用繁體中文回答 zh-TW，並用台灣人的禮貌口語表達，回答時不要特別說明這是台灣人的語氣，不要提到「台灣腔」，不用提到「逐字稿」這個詞，用「內容」代替)，回答時如果有用到數學式，請用數學符號代替純文字（Latex 用 $ 字號 render)"
            messages.append({"role": "user", "content": user_message})
        return messages

    def chat_with_service(self, service_type, system_prompt, messages):
        if service_type == 'openai':
            return self.chat_with_jutor(system_prompt, messages)
        elif service_type == 'groq_llama3':
            return self.chat_with_groq(service_type, system_prompt, messages)
        elif service_type == 'groq_mixtral':
            return self.chat_with_groq(service_type, system_prompt, messages)
        elif service_type == 'claude3':
            return self.chat_with_claude3(system_prompt, messages)
        else:
            raise gr.Error("不支持的服务类型")

    def chat_with_jutor(self, system_prompt, messages):
        messages.insert(0, {"role": "system", "content": system_prompt})
        api_endpoint = "https://ci-live-feat-video-ai-dot-junyiacademy.appspot.com/api/v2/jutor/hf-chat"
        headers = {
            "Content-Type": "application/json",
            "x-api-key": self.jutor_chat_key,
        }
        model = "gpt-4o"
        print("======model======")
        print(model)
        # model = "gpt-3.5-turbo-0125"
        data = {
            "data": {
                "messages": messages,
                "max_tokens": 512,
                "temperature": 0.9,
                "model": model,
                "stream": False,
            }
        }

        response = requests.post(api_endpoint, headers=headers, data=json.dumps(data))
        response_data = response.json()
        response_completion = response_data['data']['choices'][0]['message']['content'].strip()
        return response_completion

    def chat_with_groq(self, model_name, system_prompt, messages):
        # system_prompt insert to messages 的最前面 {"role": "system", "content": system_prompt}
        messages.insert(0, {"role": "system", "content": system_prompt})
        model_name_dict = {
            "groq_llama3": "llama-3.1-70b-versatile",
            "groq_mixtral": "mixtral-8x7b-32768"
        }
        model = model_name_dict.get(model_name)
        print("======model======")
        print(model)

        request_payload = {
            "model": model,
            "messages": messages,
            "max_tokens": 500  # 設定一個較大的值，可根據需要調整
        }
        groq_client = self.ai_client
        response = groq_client.chat.completions.create(**request_payload)
        response_completion = response.choices[0].message.content.strip()
        return response_completion

    def chat_with_claude3(self, system_prompt, messages):
        if not system_prompt.strip():
            raise ValueError("System prompt cannot be empty")
        
        model_id = "anthropic.claude-3-sonnet-20240229-v1:0"
        # model_id = "anthropic.claude-3-haiku-20240307-v1:0"
        print("======model_id======")
        print(model_id)
        kwargs = {
            "modelId": model_id,
            "contentType": "application/json",
            "accept": "application/json",
            "body": json.dumps({
                "anthropic_version": "bedrock-2023-05-31",
                "max_tokens": 500,
                "system": system_prompt,
                "messages": messages
            })
        }
        # 建立 message API，讀取回應
        bedrock_client = self.ai_client
        response = bedrock_client.invoke_model(**kwargs)
        response_body = json.loads(response.get('body').read())
        response_completion = response_body.get('content')[0].get('text').strip()
        return response_completion