Create utils.py

utils.py

@@ -0,0 +1,121 @@
+import re
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import (
+    AIMessage,
+    HumanMessage,
+    SystemMessage
+)
+
+def get_api_response(content: str, max_tokens=None):
+    
+    chat = ChatOpenAI(
+        openai_api_key=OPENAI_API_KEY,
+        #model='gpt-3',
+        #model='gpt-3.5-turbo',
+        #model='gpt-3.5-turbo-0613',
+        #model='gpt-3.5-turbo-16k',
+        model='gpt-3.5-turbo-16k-0613',
+        openai_proxy=OPENAI_Proxy,
+        #model='gpt-4',
+        #model='gpt-4-0613',
+        #model='gpt-4-32k-0613',
+        temperature=0.5）
+
+
+    # response = openai.ChatCompletion.create(
+    #     model='gpt-3.5-turbo-16k-0613',
+    #     messages=[{
+    #         'role': 'system',
+    #         'content': 'You are a helpful and creative assistant for writing novel.'
+    #     }, {
+    #         'role': 'user',
+    #         'content': content,
+    #     }],
+    #     temperature=0.5,  
+    #     max_tokens=max_tokens
+    # )
+    # response = None
+    messages = [
+        SystemMessage(content="You are a helpful and creative assistant for writing novel."),
+        HumanMessage(content=content)
+    ]
+    try:
+        response = chat(messages)
+    except:
+        st.error("OpenAI Error")
+
+    if response is not None:
+        return response.content
+    else:
+        return "Error: response not found"    
+
+def get_content_between_a_b(a,b,text):
+    return re.search(f"{a}(.*?)\n{b}", text, re.DOTALL).group(1).strip()
+
+
+def get_init(init_text=None,text=None,response_file=None):
+    """
+    init_text: if the title, outline, and the first 3 paragraphs are given in a .txt file, directly read
+    text: if no .txt file is given, use init prompt to generate
+    """
+    if not init_text:
+        response = get_api_response(text)
+        print(response)
+
+        if response_file:
+            with open(response_file, 'a', encoding='utf-8') as f:
+                f.write(f"Init output here:\n{response}\n\n")
+    else:
+        with open(init_text,'r',encoding='utf-8') as f:
+            response = f.read()
+        f.close()
+    paragraphs = {
+        "name":"",
+        "Outline":"",
+        "Paragraph 1":"",
+        "Paragraph 2":"",
+        "Paragraph 3":"",
+        "Summary": "",
+        "Instruction 1":"",
+        "Instruction 2":"", 
+        "Instruction 3":""    
+    }
+    paragraphs['name'] = get_content_between_a_b('Name:','Outline',response)
+    
+    paragraphs['Paragraph 1'] = get_content_between_a_b('Paragraph 1:','Paragraph 2:',response)
+    paragraphs['Paragraph 2'] = get_content_between_a_b('Paragraph 2:','Paragraph 3:',response)
+    paragraphs['Paragraph 3'] = get_content_between_a_b('Paragraph 3:','Summary',response)
+    paragraphs['Summary'] = get_content_between_a_b('Summary:','Instruction 1',response)
+    paragraphs['Instruction 1'] = get_content_between_a_b('Instruction 1:','Instruction 2',response)
+    paragraphs['Instruction 2'] = get_content_between_a_b('Instruction 2:','Instruction 3',response)
+    lines = response.splitlines()
+    # content of Instruction 3 may be in the same line with I3 or in the next line
+    if lines[-1] != '\n' and lines[-1].startswith('Instruction 3'):
+        paragraphs['Instruction 3'] = lines[-1][len("Instruction 3:"):]
+    elif lines[-1] != '\n':
+        paragraphs['Instruction 3'] = lines[-1]
+    # Sometimes it gives Chapter outline, sometimes it doesn't
+    for line in lines:
+        if line.startswith('Chapter'):
+            paragraphs['Outline'] = get_content_between_a_b('Outline:','Chapter',response)
+            break
+    if paragraphs['Outline'] == '':
+        paragraphs['Outline'] = get_content_between_a_b('Outline:','Paragraph',response)
+
+
+    return paragraphs
+
+def get_chatgpt_response(model,prompt):
+    response = ""
+    for data in model.ask(prompt):
+        response = data["message"]
+    model.delete_conversation(model.conversation_id)
+    model.reset_chat()
+    return response
+
+
+def parse_instructions(instructions):
+    output = ""
+    for i in range(len(instructions)):
+        output += f"{i+1}. {instructions[i]}\n"
+    return output