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context_explanation = """
As a supply chain control tower manager, my role involves overseeing the logistics network and ensuring that shipments are processed efficiently and delivered on time. The 'shipments' table in our database is crucial for monitoring these processes. It contains several columns that track the progress and timeliness of each shipment throughout various stages:

- 'Order_Date' and 'Expected_Loading_Date' provide timestamps for when an order was placed and when it was expected to be loaded for departure.
- 'Expected_Delivery_Time' is a timpestamp defining when the shipment is expected to be delivered
- 'Loading_OnTime', 'Airport_OnTime', 'Landing_OnTime', 'Transmission_OnTime' are boolean values indicating whether the shipment was processed on time at various stages. If any of these are False, it implies a delay occurred, potentially causing the shipment to miss its cut-off time and be postponed to the next day.
- 'Store_Open' indicates if the truck arrived at the store before closing tim

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# Create the prompt template
template = PromptTemplate(
    input_variables=["input_question", "context_explanation"],
    template="""{context_explanation}
    Question: {input_question}
    """
)

# Agent
agent_executor = create_sql_agent(

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user_input = "How many shipments were delayed in the first seven days of May?"
try:
    print("Question: ", user_input)
    result = agent_executor.run(user_input)
    print("Result: ", result)
except Exception as e:
    print(f"An error occurred: {e}")

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from langchain.chat_models import ChatOpenAI
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.agents.agent_types import AgentType

# Set up the OpenAI LLM
llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY, model_name = "gpt-3.5-turbo")

# Set up the SQL database connection

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,Test,Order Time,Order Date,City,Store,ShipmentID,Order Amount,Transmission OnTime,Transmission,Start PickPack,Pickpack,Loading,Expected Loading Date,Loading OnTime,Loading Date,Loading Date+1,Airport Arrival,Airport OnTime,Airport Arrival Date,Airport Arrival Date+1,Takeoff,Landing,Landing Date,Landing Date+1,Landing Date+2,Landing OnTime,Start Clearance,End Clearance,Leaving Airport,City Arrival,City Arrival Date,City Arrival Date+1,Store Open,Delivery Time,Delivery Date,Expected Delivery Time,On Time Delivery
0,1,2021-05-02 17:00:00.000000,2021-05-02,CITY2,CITY2/ST8,2021-05-02/CITY2/ST8/1,5445,False,2021-05-03 17:00:00.000000,2021-05-04 07:00:00.000000,2021-05-04 13:31:48.042144,2021-05-04 19:00:00.000000,2021-05-04,True,2021-05-04,2021-05-05,2021-05-04 21:59:12.658318,True,2021-05-04,2021-05-05,2021-05-05 06:00:00.000000,2021-05-05 19:11:16.791315,2021-05-05,2021-05-06,2021-05-07,True,2021-05-06 09:00:00.000000,2021-05-06 11:32:50.282538,2021-05-06 12:19:46.058471,2021-05-06 17:08:54.687496,2021-05-06,202

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# Define a Store class to represent a store
class Store:
    def __init__(self, location, inventory):
        self.location = location
        self.inventory = inventory
        
    def place_order(self, item, quantity):
        if item in self.inventory and self.inventory[item] >= quantity:
            self.inventory[item] -= quantity
            print("Order fulfilled.")

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# Define a Truck class to represent a truck
class Truck:
    def __init__(self, location, capacity):
        self.location = location
        self.capacity = capacity
        self.cargo = {}
        
    def load_cargo(self, item, quantity):
        if sum(self.cargo.values()) + quantity <= self.capacity:

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# Define a Warehouse class to represent a warehouse
class Warehouse:
    def __init__(self, location, capacity):
        self.location = location
        self.capacity = capacity
        self.inventory = {}
        
    def add_inventory(self, item, quantity):
        if item in self.inventory:
            self.inventory[item] += quantity

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# Add the TTS
audio_clip = AudioFileClip("Comment.mp3")
video_unload7 = video_unload1.set_audio(audio_clip)
video_unload7.write_videofile("step_1_audio.mp4")

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# Stacking with lag
final_clip_lag = clips_array([[video_unload1, video_unload3.set_start(5)],
                        [video_unload5.set_start(10), video_unload6.set_start(15)]])
final_clip_lag = final_clip_lag.volumex(0)
final_clip_lag.write_videofile("stack_lag.mp4")