Create app1.py

app1.py

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+# ---------------------------------------------------for backend looks-------------------------------------------------
+
+#with open('/usr/local/lib/python3.10/site-packages/transformers/utils/chat_template_utils.py', 'r') as file:
+    #content = file.read()
+    #print("base.py:", content)
+# ------------------------------------------------------the end--------------------------------------------------------
+
+
+
+# ===========================================
+# !-----app.py
+# ===========================================
+
+import json
+import asyncio
+import os
+import re
+import requests
+from dotenv import load_dotenv
+import chainlit as cl
+from langchain import hub
+from langchain_openai import OpenAI
+from langchain.chains import LLMChain
+from langchain_core.prompts import PromptTemplate
+from langchain.memory.buffer import ConversationBufferMemory
+
+
+load_dotenv()
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+auth_token = os.environ.get("CHAINLIT_AUTH_SECRET")
+if not auth_token.startswith("Bearer "):
+    auth_token = f"Bearer {auth_token}"
+
+# API endpoint
+API_URL = "https://aivisions.no/data/daysoff/api/v1/booking/"
+
+# LLM prompt template
+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]😊"
+"""
+daysoff_assistant_prompt = PromptTemplate(
+    input_variables=["chat_history", "question"],
+    template=daysoff_assistant_template,
+)
+
+# Async wrapper for requests.post
+async def async_post_request(url, headers, data):
+    return await asyncio.to_thread(requests.post, url, headers=headers, json=data)
+
+@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,
+    )
+
+    cl.user_session.set("llm_chain", llm_chain)
+
+@cl.on_message
+async def handle_message(message: cl.Message):
+    user_message = message.content
+    llm_chain = cl.user_session.get("llm_chain")
+    
+    booking_pattern = r'\b[A-Z]{6}\d{6}\b'   
+    match = re.search(booking_pattern, user_message)
+
+    if match:
+        
+        bestillingskode = match.group()
+        headers = {
+            "Authorization": auth_token,
+            "Content-Type": "application/json"
+        }
+        payload = {"booking_id": bestillingskode}
+
+        try:
+            
+            response = await async_post_request(API_URL, headers, payload)
+            response.raise_for_status() 
+            booking_data = response.json()
+
+            if "booking_id" in booking_data:
+                await cl.Message(
+                    content=f"""
+                    Booking Info:
+                    - Booking ID: {booking_data.get('booking_id', 'N/A')}
+                    - Full Name: {booking_data.get('full_name', 'N/A')}
+                    - Amount: {booking_data.get('amount', 0)}
+                    - 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', 0)}
+                    - Info Text: {booking_data.get('infotext', 'N/A')}
+                    - Included: {booking_data.get('included', 'N/A')}
+                    """
+                ).send()
+            else:
+                await cl.Message(content="Booking not found or invalid response.").send()
+        except requests.exceptions.RequestException as e:
+            await cl.Message(content=f"Request failed: {str(e)}").send()
+    else:
+       
+        response = await llm_chain.ainvoke(user_message)
+        await cl.Message(content=response).send()