Delete str-has-no-attribute-get-app.py

str-has-no-attribute-get-app.py

@@ -1,218 +0,0 @@
-# ===================================================
-# "the-very-latest-latest-POST-it"-----app.py
-# ===================================================
-
-import asyncio
-import os
-import re
-import time
-import json
-
-import chainlit as cl
-from dotenv import load_dotenv
-
-from pydantic import BaseModel, PrivateAttr
-
-from langchain import hub
-from langchain_openai import OpenAI
-from tiktoken import encoding_for_model
-from langchain.chains import LLMChain #APIChain
-from langchain_core.prompts import PromptTemplate
-
-from langchain_community.tools.requests.tool import RequestsPostTool
-from langchain_community.utilities.requests import TextRequestsWrapper
-
-from langchain.memory.buffer import ConversationBufferMemory
-from langchain.memory import ConversationTokenBufferMemory
-from langchain.memory import ConversationSummaryMemory
-
-from api_docs import api_docs_str
-
-load_dotenv()
-OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
-auth_token = os.getenv("DAYSOFF_API_TOKEN")
-
-
-
-class EnhancedRequestsPostTool(RequestsPostTool, BaseModel):
-    api_docs: str = api_docs_str
-
-    # --PrivateAttr@dynanmc init attrbts
-    _url_chain: LLMChain = PrivateAttr()
-    _response_chain: LLMChain = PrivateAttr()
-
-    def __init__(self, requests_wrapper, llm, api_docs_str, api_url_prompt, api_response_prompt):
-        super().__init__(requests_wrapper=requests_wrapper, allow_dangerous_requests=True)
-        object.__setattr__(self, 'api_docs', api_docs_str) # self.api_docs = api_docs_str
-        object.__setattr__(self, 'url_chain', LLMChain(llm=llm, prompt=api_url_prompt))  # --dynanmc init1
-        object.__setattr__(self, 'response_chain', LLMChain(llm=llm, prompt=api_response_prompt)) # --dynanmc init2
-
-    async def ainvoke(self, input_data, callbacks=None):
-        # -- create API URL
-        url_response = await self.url_chain.ainvoke({
-            "api_docs": self.api_docs,
-            "question": input_data.get("question")
-        })
-
-        api_response = await super().ainvoke(input_data, callbacks) # --make POST request
-
-        # --form(at) response/:response_chain
-        formatted_response = await self.response_chain.ainvoke({
-            "api_docs": self.api_docs,
-            #"question": input_data.get("question"),
-            #"api_url": url_response.get("text"),
-            "api_response": api_response
-        })
-
-        return formatted_response
-
-daysoff_assistant_template = """
-#You are a customer support assistant (’kundeservice AI assistent’) for Daysoff.
-#By default, you respond in Norwegian language, using a warm, direct, and professional tone.
-Your expertise is exclusively in retrieving booking information for a given booking ID assistance related to
-to this.
-You do not provide information outside of this scope. If a question is not about this topic, respond with
-"Jeg driver faktisk kun med henvendelser omkring bestillingsinformasjon. Gjelder det andre henvendelser
-må du nok kontakte kundeservice på [email protected]😊"
-Chat History: {chat_history}
-Question: {question}
-Answer:
-"""
-daysoff_assistant_prompt = PromptTemplate(
-    input_variables=['chat_history', 'question'],
-    template=daysoff_assistant_template
-)
-
-api_url_template = """
-Given the following API Documentation for Daysoff's official
-booking information API: {api_docs}
-Your task is to construct the most efficient API URL to answer
-the user's question, ensuring the
-call is optimized to include only the necessary information.
-Question: {question}
-API URL:
-"""
-api_url_prompt = PromptTemplate(input_variables=['api_docs', 'question'],
-                                template=api_url_template)
-
-api_response_template = """
-With the API Documentation for Daysoff's official API: {api_docs} in mind,
-and the specific user question: {question},
-and given this API URL: {api_url} for querying,
-and response from Daysoff's API: {api_response},
-never refer the user to the API URL as your answer!
-You should always provide a clear and concise summary (in Norwegian) of the booking information retrieved.
-This way you directly address the user's question in a manner that reflects the professionalism and warmth
-of a human customer service agent.
-Summary:
-"""
-api_response_prompt = PromptTemplate(
-    input_variables=['api_docs', 'question', 'api_url', 'api_response'],
-    template=api_response_template
-)
-
-
-@cl.on_chat_start
-def setup_multiple_chains():
-    llm = OpenAI(
-        model='gpt-3.5-turbo-instruct',
-        temperature=0.7,
-        openai_api_key=OPENAI_API_KEY,
-        max_tokens=2048,
-        top_p=0.9,
-        frequency_penalty=0.1,
-        presence_penalty=0.1
-    )
-
-    conversation_memory = ConversationBufferMemory(memory_key="chat_history",
-                                                   max_len=30,
-                                                   return_messages=True,
-    )
-
-    llm_chain = LLMChain(
-        llm=llm,
-        prompt=daysoff_assistant_prompt,
-        memory=conversation_memory,
-    )
-
-    requests_wrapper = TextRequestsWrapper(
-        headers={
-            "Authorization": f"Bearer <auth_token>",
-            "Content-Type": "application/json"
-        }
-    )
-
-    post_tool = EnhancedRequestsPostTool(
-        requests_wrapper=requests_wrapper,
-        llm=llm,
-        api_docs_str=api_docs_str,
-        api_url_prompt=api_url_prompt,
-        api_response_prompt=api_response_prompt
-    )
-
-    cl.user_session.set("llm_chain", llm_chain)
-    cl.user_session.set("post_tool", post_tool)
-
-
-@cl.on_message
-async def handle_message(message: cl.Message):
-    user_message = message.content
-    llm_chain = cl.user_session.get("llm_chain")
-    post_tool = cl.user_session.get("post_tool")
-
-    booking_pattern = r'\b[A-Z]{6}\d{6}\b'
-    endpoint_url = "https://aivisions.no/data/daysoff/api/v1/booking/"
-
-    match = re.search(booking_pattern, user_message)
-    if match:
-        
-        bestillingskode = match.group()
-        post_data = {
-            "url": endpoint_url,
-            "body": { 
-                "booking_id": bestillingskode
-            }
-        }
-
-        response = await post_tool.ainvoke(
-            json.dumps(post_data),
-            [cl.AsyncLangchainCallbackHandler()]  # config={"callbacks": 
-        )
-
-        # --degug!
-        print(f"Raw response: {response}")
-
-        if response:
-            try:
-                booking_data = json.loads(response.get("output", "{}"))
-                table = "| Field | Information |\n|---|---|\n"  
-
-                table = f"""
-                | Field       | Value              |
-                |-------------|--------------------|
-                | Booking ID  | {booking_data.get('booking_id', 'N/A')} |
-                | Name        | {booking_data.get('full_name', 'N/A')} |
-                | Amount      | {booking_data.get('amount', 'N/A')} kr |
-                | Check-in    | {booking_data.get('checkin', 'N/A')} |
-                | Check-out   | {booking_data.get('checkout', 'N/A')} |
-                | Address     | {booking_data.get('address', 'N/A')} |
-                | User ID     | {booking_data.get('user_id', 'N/A')} |
-                | Info        | {booking_data.get('infotext', 'N/A')} |
-                | Included    | {booking_data.get('included', 'N/A')} |
-                """
-                await cl.Message(content=table).send()
-                
-            except Exception as e:
-                error_msg = f"Error: Could not parse the booking information. Details: {str(e)}"
-            await cl.Message(error_msg).send()
-
-    else:
-        response = await llm_chain.ainvoke(
-            user_message,                                # {"chat_history": "", "question": user_message}
-        )
-        await cl.Message(content=response).send()
-
-    response_key = "output" if "output" in response else "text"
-
-    await cl.Message(response.get(response_key, "")).send()
-    return message.content