import gradio as gr import json import os from openai import OpenAI import re from azure.cognitiveservices.speech import SpeechConfig, SpeechSynthesizer, AudioConfig PASSWORD = os.environ['PASSWORD'] OPEN_AI_KEY = os.environ['OPEN_AI_KEY'] AZURE_REGION = os.environ['AZURE_REGION'] AZURE_API_KEY = os.environ['AZURE_API_KEY'] def validate_and_correct_chat(data, roles=["A", "B"], rounds=2): """ Corrects the chat data to ensure proper roles and number of rounds. Parameters: - data (list): The chat data list of dicts, e.g. [{"role": "A", "content": "Hi"}, ...] - roles (list): The expected roles, default is ["A", "B"] - rounds (int): The number of rounds expected Returns: - list: Corrected chat data """ # Validate role names for item in data: if item['role'] not in roles: print(f"Invalid role '{item['role']}' detected. Correcting it.") # We will change the role to the next expected role in the sequence. prev_index = roles.index(data[data.index(item) - 1]['role']) next_index = (prev_index + 1) % len(roles) item['role'] = roles[next_index] # Validate number of rounds expected_entries = rounds * len(roles) if len(data) > expected_entries: print(f"Too many rounds detected. Trimming the chat to {rounds} rounds.") data = data[:expected_entries] return data def extract_json_from_response(response_text): # 使用正則表達式匹配 JSON 格式的對話 match = re.search(r'\[\s*\{.*?\}\s*\]', response_text, re.DOTALL) if match: json_str = match.group(0) return json.loads(json_str) else: raise ValueError("JSON dialogue not found in the response.") def create_chat_dialogue(rounds, role1, role1_gender, role2, role2_gender, theme, language, cefr_level): client = OpenAI(api_key=OPEN_AI_KEY) # 初始化對話 sentenses_count = int(rounds) * 2 sys_content = f"你是一個{language}家教，請用{language}生成對話" prompt = f"您將進行一場以{theme}為主題的對話，請用 cefr_level：{cefr_level} 為對話的程度。{role1} (gender: {role1_gender}) 和{role2} (gender: {role2_gender})將是參與者。請依次交談{rounds}輪。(1輪對話的定義是 {role1} 和 {role2} 各說一句話，總共 {sentenses_count} 句話。)以json格式儲存對話。並回傳對話JSON文件。格式為：[{{role:\"{role1}\", \"gender\": {role1_gender} , content: \".....\"}}, {{role:\"{role2}\", \"gender\": {role2_gender}, content: \".....\"}}]" messages = [ {"role": "system", "content": sys_content}, {"role": "user", "content": prompt} ] print("=====messages=====") print(messages) print("=====messages=====") request_payload = { "model": "gpt-4-1106-preview", "messages": messages, "max_tokens": int(500 * int(rounds)) # 設定一個較大的值，可根據需要調整 } response = client.chat.completions.create(**request_payload) print(response) response_text = response.choices[0].message.content.strip() extract_json = extract_json_from_response(response_text) dialogue = validate_and_correct_chat(data=extract_json, roles=[role1, role2], rounds=rounds) print(dialogue) # 這裡直接返回JSON格式的對話，但考慮到這可能只是一個字符串，您可能還需要將它解析為一個Python對象 return dialogue def generate_dialogue(rounds, method, role1, role1_gender, role2, role2_gender, theme, language, cefr_level): if method == "auto": dialogue = create_chat_dialogue(rounds, role1, role1_gender, role2, role2_gender, theme, language, cefr_level) else: dialogue = [{"role": role1, "gender": role1_gender, "content": "手動輸入文本 1"}, {"role": role2, "gender": role2_gender , "content": "手動輸入文本 2"}] return dialogue def main_function(password: str, theme: str, language: str, cefr_level: str, method: str, rounds: int, role1: str, role1_gender: str, role2: str, role2_gender: str): if password != os.environ.get("PASSWORD", ""): return "错误的密码，请重新输入。", "" structured_dialogue = generate_dialogue(rounds, method, role1, role1_gender, role2, role2_gender, theme, language, cefr_level) # Convert structured dialogue for Chatbot component to show "role1: content1" and "role2: content2" side by side chatbot_dialogue = [] for i in range(0, len(structured_dialogue), 2): # We iterate with a step of 2 to take pairs # Get the content for the two roles in the pair role1_content = f"{structured_dialogue[i]['content']}" role2_content = f"{structured_dialogue[i+1]['content']}" if i+1 < len(structured_dialogue) else "" chatbot_dialogue.append((role1_content, role2_content)) # audio_path = dialogue_to_audio(structured_dialogue, role1_gender, role2_gender) json_output = json.dumps({"dialogue": structured_dialogue}, ensure_ascii=False, indent=4) # 儲存對話為 JSON 文件 file_name = "dialogue_output.txt" with open(file_name, "w", encoding="utf-8") as f: f.write(json_output) return chatbot_dialogue, file_name, json_output if __name__ == "__main__": with gr.Blocks(theme=gr.themes.Soft()) as demo: # 使用 'light' 主题作为默认值 # Header 或其他组件可以在这里添加，如果有需要 with gr.Row(): with gr.Column(scale=2): # 2/3 的宽度 chat_output = gr.Chatbot(label="生成的對話") json_file = gr.File(label="下載對話 JSON 文件") json_textbox = gr.Textbox(readonly=True, label="對話 JSON 內容", lines=10) with gr.Column(scale=1): # 1/3 的宽度 password = gr.Textbox(label="输入密码", type="password") theme = gr.Textbox(label="對話主題") # 加入 theme 的輸入框，設定預設值為 '購物' language = gr.Dropdown(choices=["中文", "英文"], label="語言") cefr_level = gr.Dropdown(choices=["A1", "A2", "B1", "B2", "C1", "C2"], label="CEFR Level") generation_mode = gr.Dropdown(choices=["auto", "manual"], label="生成方式") rounds = gr.Slider(minimum=2, maximum=6, step=2, label="對話輪數") role1_name = gr.Textbox(label="角色 1 名稱") role1_gender = gr.Dropdown(choices=["male", "female"], label="角色 1 性別") role2_name = gr.Textbox(label="角色 2 名稱") role2_gender = gr.Dropdown(choices=["male", "female"], label="角色 2 性別") # 在这里添加提交和清除按鈕 submit_button = gr.Button("Submit") clear_button = gr.Button("Clear") submit_button.click( main_function, [ password, theme, language, cefr_level, generation_mode, rounds, role1_name, role1_gender, role2_name, role2_gender ], [ chat_output, json_file, json_textbox ] ) clear_button.click(lambda: [[],None,""], None, [chat_output, json_file, json_textbox], queue=False) # 可以添加其他交互逻辑和按钮事件，如果有需要 demo.launch(inline=False, share=True)