Update main.py

main.py

@@ -1,154 +1,100 @@
-from fastapi import FastAPI, Request, HTTPException
-from fastapi.responses import PlainTextResponse
-from fastapi.middleware.cors import CORSMiddleware
-from twilio.twiml.messaging_response import MessagingResponse
-import os,openai
-import google.generativeai as genai
-from llama_index.llms import OpenAI
-from llama_index import VectorStoreIndex, SimpleDirectoryReader
-from llama_index.llms import OpenAI
-from llama_index import StorageContext, load_index_from_storage
+import os 
+import aiohttp
+import asyncio
+import requests
+from fastapi import FastAPI, HTTPException
+import openai
 
-secret = os.environ["key"]
-
-genai.configure(api_key=secret)
-model = genai.GenerativeModel('gemini-1.5-flash')
-
-import user_guide_sync
-
-global index,query_engine
-query_engine = index = None
-#query_engine = (user_guide_sync.update_user_guide).as_query_engine()
 
+# Initialize FastAPI app
 app = FastAPI()
 
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-@app.post("/update_user_guide_data")
-async def update_user_guide_data():
-    user_guide_sync.update_user_guide()
-    return "guide updated"
-
-
-
-index = None
-
-@app.post("/whatsapp")
-async def reply_whatsapp(request: Request):
-    
-    form_data = await request.form()
-    num_media = int(form_data.get("NumMedia", 0))
-    from_number = form_data.get("From")
-    message_body = form_data.get("Body")
-    user_query = message_body
-
-    response = MessagingResponse()
-    #msg.media(GOOD_BOY_URL)
-    try:
-        openai.api_key = os.environ["OPENAI_API_KEY"]
-
-        # Define the messages for the conversation
-        messages = [
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": f"always translate to english(only): '{user_query}'   give only translated part only without any extra details"  }
+# Set your OpenAI API key here
+openai.api_key = os.environ["OPENAI_API_KEY"]
+
+# Authorization headers
+AUTH_HEADERS = {
+    'X-Tenant': 'royalexpress',
+    'Accept': 'application/json',
+    'Authorization': 'Bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJhdWQiOiIxIiwianRpIjoiYjczMDAxYzMyMTE1ZGIyNTY4ODUzMTg2OWVjMzUwNTdjZmE3YTJmN2I3ZWZjMjQzNjdmZTA2ZTk4ZjY4ZjMwMTE0ZTIzOGUwY2I0ZmQ0YjIiLCJpYXQiOjE3MzA3ODIxNjEuMjkyNDM0LCJuYmYiOjE3MzA3ODIxNjEuMjkyNDM3LCJleHAiOjQ4ODY0NTU3NjEuMDc2ODExLCJzdWIiOiIxIiwic2NvcGVzIjpbXX0.I4wGFzoepmAC2RaADetE95BdbY4AYPUfUouFepVthZq_KewQLoYEiYMmxErgAOvYDL9IdhTg8pHm3KtCjtfF79Toigvzl-4RIYE9qwavVYabUaIMtxdkvLmzC2uSSxNkQ-Jx4ZsEVt34NpMMZ6ZsMsgszkreed_s7i5I6ek6T2-p9cZYPpFfGhlRIrgAhOL1yZe0t5HQMM7P1cULB7IMb3s0fvwLNBimPC4Iznick5o2lWO6KcubsKSAMyPwBaCQhjGTKd0eJCde1IvL8mEaMvhu8v853AIDSiBsC83hjK41hPAaiBHeev1JjdDhEd6_qO9dpucKaGCqYiVfBFH_pgnynErmhKlPEIz7sZlBWz8zxISDW5PRo9d-jXRP-A31W76Q3H-ZKfnam0D8yYFY0EIZHhvgvZUl3r0dR4PRh7PYlNZgnyfAcAYmK9Bektjbbx5RuzH6gtT9hLQrxYiQNg0irCNwgTYnuQ4AjPA3BpZuOfWtygeDZKgv1gnveTzMJG7T6s95k8yNSNT1_OfRQONPX8LBasRwZWCGkWj7fopO6K8gcrEU5FIpql0UviwGJOTZeFmqwWJ1AIcOM0MHWNp--Y8evHrvuNGk3SDcjBcvhF58I2Hd5F4MefN_ZB9N7oxUUDBYbxnTH6SN7Wx-VsluEOlf9ShfBNvHZaUi61E'
+}
+
+# Helper function to get the order ID from the waybill number
+def get_order_id(waybill_no):
+    url = f"https://dev3.api.curfox.parallaxtec.com/api/ml/order/{waybill_no}"
+    response = requests.get(url, headers=AUTH_HEADERS)
+    if response.status_code == 200:
+        return response.json().get("data", {}).get("id")
+    else:
+        raise HTTPException(status_code=404, detail="Order ID not found")
+
+# Asynchronous function to fetch data concurrently
+async def fetch_data(session, url):
+    async with session.get(url, headers=AUTH_HEADERS) as response:
+        return await response.text()
+
+# Asynchronous function to get all order data
+async def get_all_order_data(order_id):
+    urls = [
+        f"https://dev3.api.curfox.parallaxtec.com/api/ml/order/{order_id}/time-line",
+        f"https://dev3.api.curfox.parallaxtec.com/api/ml/order/{order_id}/finance-time-line",
+        f"https://dev3.api.curfox.parallaxtec.com/api/ml/order/{order_id}/invoice-time-line"
+    ]
+
+    async with aiohttp.ClientSession() as session:
+        tasks = [fetch_data(session, url) for url in urls]
+        responses = await asyncio.gather(*tasks)
+
+    full_data = {
+        "Timeline Details": responses[0],
+        "Finance Time Line": responses[1],
+        "Invoice Time Line": responses[2]
+    }
+    return full_data
+
+# Function to interact with OpenAI API
+def ask_openai(messages):
+    response = openai.ChatCompletion.create(model="gpt-4o-mini", messages=messages)
+    return response.choices[0].message['content']
+
+# Main endpoint to handle user queries
+@app.post("/process_query/")
+async def process_query(query: str):
+    # Initial message to check for waybill number in the query
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant for Curfox delivery system."},
+        {"role": "user", "content": f"""Check if the order number is present in the query (e.g., CA000001). 
+                                        Respond 'done' if present, otherwise prompt the user to provide the number."""}
+    ]
+    result = ask_openai(messages)
+
+    if result == "done":
+        # Extract the waybill number
+        extract_message = [
+            {"role": "system", "content": "You are a helpful assistant for Curfox delivery system."},
+            {"role": "user", "content": f"Extract the waybill number from the query. User query: {query}"}
         ]
-        # Make the API call
-        gptresponse = openai.chat.completions.create(
-            model="gpt-4o",
-            messages=messages
-        )
-
-        user_query = gptresponse.choices[0].message.content
-
-        print(f"translated text : {user_query}")
-
-
-        
-        global index  # Use the global index variable
-        if index is None:  # Check if the index is already loaded
-                from llama_index import StorageContext, load_index_from_storage  # Import necessary modules
-                storage_context = StorageContext.from_defaults(persist_dir="llama_index")
-                index = load_index_from_storage(storage_context=storage_context)
-                print("Index loaded")
-        else:
-                print("Index already loaded")
-        # Set up a retriever to fetch similar documents directly without full query processing
-        retriever = index.as_retriever()
-        # Retrieve the top similar documents based on the user query
-        similar_docs = retriever.retrieve(user_query)  # Adjust `top_k` as needed
-        # Prepare the context for LLM by concatenating the content of similar documents
-        context = "\n\n".join([doc.node.text for doc in similar_docs])
-
-        prompt = f"""
-        context : {context}
-        
-        user query : {user_query}
-        
-                Instructions:
-                - First, understand the user question carefully.
-                - If you find the correct answer from the provided data, respond with detailed steps (1, 2, ...) and always include a more details link.
-                - If the correct answer is not found in the provided data or proide the correct solution to user using data then output is only this : "contact our help desk". dont add extra anything
-         """
-        messages = [
-            {"role": "user", "content": prompt  }
+        waybill_number = ask_openai(extract_message)
+
+        # Fetch order ID and order details
+        try:
+            order_id = get_order_id(waybill_number)
+            full_data = await get_all_order_data(order_id)
+        except HTTPException as e:
+            return {"error": str(e.detail)}
+
+        # Generate final response based on collected data
+        response_message = [
+            {"role": "system", "content": "You are a helpful assistant for Curfox delivery system."},
+            {"role": "user", "content": f"Answer based on the provided data only. Data: {full_data}. User query: {query}"}
         ]
-        
-        # Make the API call
-        gptresponse = openai.chat.completions.create(
-            model="gpt-4o-mini",
-            messages=messages
-        )
-        gpt_response = gptresponse.choices[0].message.content
-        
-        print(str(gpt_response).lower())
-        if "contact our help desk" in str(gpt_response).lower() or "our help desk" in str(gpt_response).lower():
-                print("help desk option")
-    
-                
-                prompt = f"""
-                         system:
-                         you are parallax technologies chatbot design for answer the user question like a real human.
-                         contact details suppor team link : https://projects.storemate.lk/customer  Email : [email protected] Youtube : https://www.youtube.com/channel/UCFkX9Fa-Qe6Qi4V5f0RcfSA Facebook : https://www.facebook.com/storemateinventory web link : https://storemate.lk
-                         
-                         only give single answer and don't give answer for general answers(this is CRM system for only pos system clients)
-                         note : don't give any steps for solve the issues but give steps for sytem slow and performance related questions
-                         user:{user_query}
-                         """
-                messages = [
-                    {"role": "system", "content": "you are parallax technologies chatbot design for answer the user question like a real human"},
-                    {"role": "user", "content": prompt}
-                ]
-
-                #gen_response = model.generate_content(prompt)
-            
-                gpt_response = openai.chat.completions.create(
-                
-                model="gpt-4o-mini",
-                
-                messages=messages,
-                
-                temperature=0,
-                
-                )
-                
-                response.message(str(gpt_response.choices[0].message.content))
-                #response.message(gen_response.text)
-                #response.message(gen_response.text +"\n\n"+default)
-                return PlainTextResponse(str(response), media_type="application/xml")
-
-        response.message(str(gpt_response)) 
-        #response.message("https://storemate.lk")
-        return PlainTextResponse(str(response), media_type="application/xml")   
-    except Exception as e:
-        print(str(e))
-        response.message("please ask again...!")
-        return PlainTextResponse(str(response), media_type="application/xml")  
-    
-
-# Run the application (Make sure you have the necessary setup to run FastAPI)
+        final_answer = ask_openai(response_message)
+        return {"answer": final_answer}
+    else:
+        # If no order number is found, prompt the user
+        return {"message": result}
+
+# Start the FastAPI app
+# Run the app on Colab using Uvicorn
+# import uvicorn
+# uvicorn.run(app, host="0.0.0.0", port=8000)