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@bot.command()
async def dogsplain(ctx, *, question: str):
    print(f"User: {ctx.author.name}, Query: {question}")
    try:
        response = chat_bot.query(question)
        await send_response(ctx, response)
    except Exception as e:
        await send_response(ctx, "An error occurred. Please try again!")
        print("Error occurred during 'query' command:", e)

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import llama_index
from llama_index.tools import QueryEngineTool, ToolMetadata

from llama_index import (
    SimpleDirectoryReader,
    VectorStoreIndex,
    StorageContext,
    load_index_from_storage,
)
try:

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query_engine_tools = [
    QueryEngineTool(
        query_engine=tesla_engine,
        metadata=ToolMetadata(
            name="tesla_tool",
            description=(
                "Provides information about Teslas predictions for future "
                "Use a detailed plain text question as input to the tool."
            ),
        ),

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from llama_index.agent import OpenAIAgent
from llama_index import load_index_from_storage, StorageContext
from llama_index.node_parser import SentenceSplitter

# Initialize the SentenceSplitter node parser
node_parser = SentenceSplitter()

#load documents and build vector index
for idx, patent_title in enumerate(patent_titles):
    file_path = os.path.join(patents_dir, f"{patent_title}.txt")

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# Create vector_index instance
vector_index = VectorStoreIndex(nodes, service_context=service_context)

# Build the summary index
summary_index = SummaryIndex(nodes, service_context=service_context)

# Now you can safely define query engines since vector_index is defined
vector_query_engine = vector_index.as_query_engine()
summary_query_engine = summary_index.as_query_engine()

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from llama_index.agent import FnRetrieverOpenAIAgent
from llama_index.llms import OpenAI

# Initialize the LLM
llm = OpenAI(model="gpt-3.5-turbo-0613")

# Initialize the FnRetrieverOpenAIAgent
top_agent = FnRetrieverOpenAIAgent.from_retriever(
   obj_index.as_retriever(similarity_top_k=4),
   system_prompt=""" \

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from tensorflow.keras.applications import VGG16
from tensorflow.keras import layers, models
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import BinaryCrossentropy
from tensorflow.keras.metrics import BinaryAccuracy

# Load the pre-trained VGG16 model without the top layer
pretrained = VGG16(input_shape=(256, 256, 3), include_top=False, weights="imagenet")
pretrained.trainable = False

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import tensorflow as tf
from tensorflow.keras.applications import VGG16
from tensorflow.keras import layers, models, optimizers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import EarlyStopping

# Load the pre-trained VGG16 model without the top layer
base_model = VGG16(input_shape=(256, 256, 3), include_top=False, weights='imagenet')

# Freeze the convolutional base

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import tensorflow as tf
from tensorflow.keras.models import load_model
import gradio as gr
import numpy as np
from PIL import Image

# Load the saved model
model = load_model("modelVGG16.h5")

# Define the prediction function

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from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
from config import QDRANT_API_KEY, QDRANT_URL

def get_qdrant_client():
    qdrant_client = QdrantClient(
        url=QDRANT_URL,
        api_key=QDRANT_API_KEY,
    )
    # Ensure the collection exists