code stringlengths 21 2.82k | instruction stringlengths 18 1.52k |
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# import dependencies
from ultralytics import YOLO
import cv2
# load a pretrained model
model = YOLO("yolov8n.pt")
# read the image
img = cv2.imread("image1.jpg")
# inferance the model
results = model.predict(source=img,conf=0.25 , classes=0, save=False)
boxes = results[0].boxes.xyxy.cpu().numpy().astype(int)
# l... | Can you provide a python script that uses the YOLOv8 model from the Ultralytics library to detect people in an image, draw green bounding boxes around them, and then save the image? |
# import dependencies
from ultralytics import YOLO
import cv2
# load a pretrained model
model = YOLO("yolov8n.pt")
# read the image
img = cv2.imread("image1.jpg")
# inferance the model
results = model.predict(source=img,conf=0.25 , classes=0, save=False)
boxes = results[0].boxes.xyxy.cpu().numpy().astype(int)
# l... | How can I use Python, YOLOv8, and OpenCV to identify people in an image, draw green bounding boxes on the detected people, and save the result? |
# import dependencies
import cv2
from ultralytics import YOLO
import time
# load a pretrained model
model = YOLO('yolov8n.pt')
pTime = 0
frame_no = 0
video_path = "car_vid.mp4"
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
# read the each frame
success, frame = cap.read()
if success... | Can you provide a Python script that uses YOLOv8 to count the number of cars in each frame of a video, draw bounding boxes around them, display the car count, FPS, and frame number on the image, and finally display the video output? |
# import dependencies
import cv2
from ultralytics import YOLO
import time
# load a pretrained model
model = YOLO('yolov8n.pt')
pTime = 0
frame_no = 0
video_path = "car_vid.mp4"
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
# read the each frame
success, frame = cap.read()
if success... | How can I write a Python code using YOLOv8 to count and draw bounding boxes around cars in each frame of a video, display the car count, FPS, and frame number on the image, and then display the video output? |
# import dependencies
from ultralytics import YOLO
# load a pretrained model
model = YOLO('yolov8n.pt')
"""
---------safty_dataset folder structure and content of the data.yaml should be ------
safty_dataset
- test
-images
-labels
- train
-images
... | Can you provide a python script to train the yolov8-nano model on a custom safety equipment dataset? |
# import dependencies
from ultralytics import YOLO
# load a pretrained model
model = YOLO('yolov8n.pt')
"""
---------safty_dataset folder structure and content of the data.yaml should be ------
safty_dataset
- test
-images
-labels
- train
-images
... | How can I train the yolov8-nano model using a custom safety equipment dataset in Python? |
# import dependencies
from ultralytics import YOLO
# load the custom model
model = YOLO('path//to/custom/trained/model/best.pt')
"""
--------- safty_dataset folder structure and content of the data.yaml should be ------
safty_dataset
- test
-images
-labels
- train
... | How can I validate a custom trained yolov8 model and print the MAP values using python? |
# import dependencies
from ultralytics import YOLO
# load the custom model
model = YOLO('path//to/custom/trained/model/best.pt')
"""
--------- safty_dataset folder structure and content of the data.yaml should be ------
safty_dataset
- test
-images
-labels
- train
... | What is the python code to validate a yolov8 custom trained model and display the MAP values? |
# import dependencies
import cv2
from ultralytics import YOLO
# load the custom model
model = YOLO('yolov8n.pt')
video_path = "car_vid.mp4"
cap = cv2.VideoCapture(video_path)
# get the frame_width and frame_height
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIG... | Can you provide a Python code snippet that uses the Botsort tracking algorithm to identify and track cars in a video footage? The code should draw bounding boxes and track-ids on each detected car and save the output video. |
# import dependencies
import cv2
from ultralytics import YOLO
# load the custom model
model = YOLO('yolov8n.pt')
video_path = "car_vid.mp4"
cap = cv2.VideoCapture(video_path)
# get the frame_width and frame_height
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIG... | How can I use Python and the Botsort tracking algorithm to track cars in a video, draw bounding boxes and track-ids on each detected car, and save the result as a video? |
# import dependencies
import cv2
from ultralytics import YOLO
# load the custom model
model = YOLO('yolov8n.pt')
video_path = "car_vid.mp4"
cap = cv2.VideoCapture(video_path)
car_count= set()
while cap.isOpened():
# read the each video frame
success, frame = cap.read()
if success:
... | Can you provide a Python code snippet that uses the ByteTrack tracking algorithm to count the number of cars in a video footage and print the total count at the end? |
# import dependencies
import cv2
from ultralytics import YOLO
# load the custom model
model = YOLO('yolov8n.pt')
video_path = "car_vid.mp4"
cap = cv2.VideoCapture(video_path)
car_count= set()
while cap.isOpened():
# read the each video frame
success, frame = cap.read()
if success:
... | How can I write a Python code using ByteTrack tracking algorithm to count cars in a video and print the total number of cars at the end of the code? |
# clone the github repo
!git clone https://github.com/ultralytics/yolov5
%cd yolov5
# install the packages
!pip install -qr requirements.txt comet_ml # install the packages
"""
here is the available sourse options
--source 0 # webcam
img.jpg # image
vid.mp4 # video
... | Can you provide the steps and commands to use yolov5 for object detection via CLI? |
# clone the github repo
!git clone https://github.com/ultralytics/yolov5
%cd yolov5
# install the packages
!pip install -qr requirements.txt comet_ml # install the packages
"""
here is the available sourse options
--source 0 # webcam
img.jpg # image
vid.mp4 # video
... | What are the necessary CLI commands and steps to perform object detection using yolov5? |
# clone the github repo
!git clone https://github.com/ultralytics/yolov5
%cd yolov5
# install the packages
!pip install -qr requirements.txt comet_ml # install the packages
# Download COCO val
torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 imag... | What are the steps to validate the yolov5 pretrained model using the coco val dataset? |
# clone the github repo
!git clone https://github.com/ultralytics/yolov5
%cd yolov5
# install the packages
!pip install -qr requirements.txt comet_ml # install the packages
# Download COCO val
torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 imag... | Can you provide the CLI commands to validate the yolov5 pretrained model with the coco val dataset? |
# clone the github repo
!git clone https://github.com/ultralytics/yolov5
%cd yolov5
# install the packages
!pip install -qr requirements.txt comet_ml # install the packages
"""
* Train a YOLOv5s model on the COCO128 dataset with --data coco128.yaml,
starting from pretrained --weights... | Can you provide the steps and commands to train a custom yolov5 model for the coco128 dataset using CLI? |
# clone the github repo
!git clone https://github.com/ultralytics/yolov5
%cd yolov5
# install the packages
!pip install -qr requirements.txt comet_ml # install the packages
"""
* Train a YOLOv5s model on the COCO128 dataset with --data coco128.yaml,
starting from pretrained --weights... | What are the necessary CLI commands and instructions to train a yolov5 model on the coco128 dataset? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.yaml') # build a new model from YAML
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n-cls.yaml').load('yolov8n-cls.pt') # build from YAML and transfer weights
# Train t... | Can you provide a Python code snippet and CLI commands to train a YOLOv8n-cls model on the MNIST160 dataset for 100 epochs with an image size of 64? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.yaml') # build a new model from YAML
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n-cls.yaml').load('yolov8n-cls.pt') # build from YAML and transfer weights
# Train t... | How can I train a YOLOv8n-cls model on the MNIST160 dataset for 100 epochs at an image size of 64 using Python and CLI commands? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.top1 # top1 accuracy
metrics.top5... | Can you provide a Python code snippet to validate the accuracy of a trained YOLOv8n-cls model on the MNIST160 dataset? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.top1 # top1 accuracy
metrics.top5... | How can I validate the accuracy of a trained YOLOv8n-cls model on the MNIST160 dataset using Python? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
# Or either you can use CLI
yolo c... | Can you provide a Python code snippet that demonstrates how to use a trained YOLOv8n-cls model for image prediction? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
# Or either you can use CLI
yolo c... | How can I use a trained YOLOv8n-cls model to run predictions on images using Python? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.yaml') # build a new model from YAML
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
# Train the model
results... | Can you provide a Python code snippet to train a YOLOv8n model on the COCO128 dataset for 100 epochs with an image size of 640? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.yaml') # build a new model from YAML
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
# Train the model
results... | How can I train a YOLOv8n model on the COCO128 dataset for 100 epochs at an image size of 640 using Python? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
# Or either you can use CLI
yolo detec... | Can you provide a Python code snippet that demonstrates how to use a trained YOLOv8n model to run predictions on images? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
# Or either you can use CLI
yolo detec... | How can I use a trained YOLOv8n model to make predictions on images using Python? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom trained model
# Export the model
model.export(format='onnx')
# Or either you can use CLI
yolo export model=yolov8n.pt format=onnx # export official m... | How can I convert a YOLOv8n model to ONNX format using Python? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom trained model
# Export the model
model.export(format='onnx')
# Or either you can use CLI
yolo export model=yolov8n.pt format=onnx # export official m... | What is the Python code to export a YOLOv8n model to a different format like ONNX? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.yaml') # build a new model from YAML
model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n-seg.yaml').load('yolov8n.pt') # build from YAML and transfer weights
# Train the m... | Can you provide a python code to train a YOLOv8n-seg model on the COCO128-seg dataset for 100 epochs with an image size of 640? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.yaml') # build a new model from YAML
model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n-seg.yaml').load('yolov8n.pt') # build from YAML and transfer weights
# Train the m... | How can I train a YOLOv8n-seg model on the COCO128-seg dataset for 100 epochs at an image size of 640 using python? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.box.map # map50-95(B)
metrics.bo... | Can you provide a Python script to evaluate the performance of a trained YOLOv8n-seg model on the COCO128-seg dataset? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.box.map # map50-95(B)
metrics.bo... | How can I validate the accuracy of a YOLOv8n-seg model that has been trained on the COCO128-seg dataset using Python? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
# Or either you can use CLI
yolo s... | Can you provide a Python code snippet that demonstrates how to use a trained YOLOv8n-seg model for image prediction? |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
# Or either you can use CLI
yolo s... | How can I use a trained YOLOv8n-seg model to run predictions on images using Python? |
pip install openai
import os
import openai
import requests
import json
openai.api_key = "xxxxxxxxxxxxxxxxxx"
url = "https://api.openai.com/v1/chat/completions"
payload = json.dumps({
"model": "gpt-4-0613",
"messages": [
{
"role": "user",
"content": "Send Cobus from HumanFirst AI an email and ask ... | Can you provide me with a Python script that uses the OpenAI API to send an email to a specific recipient, with a specific body, at a specific date and time? |
pip install openai
import os
import openai
import requests
import json
openai.api_key = "xxxxxxxxxxxxxxxxxx"
url = "https://api.openai.com/v1/chat/completions"
payload = json.dumps({
"model": "gpt-4-0613",
"messages": [
{
"role": "user",
"content": "Send Cobus from HumanFirst AI an email and ask ... | How can I use Python and OpenAI API to schedule an email to be sent to a specific address, with a specific content, on a specific date and time? |
# Build the messages array
messages =[
{"role": "system", "content": system_message},
{"role": "user", "content": user_message},
]
# Call the Azure OpenAI model
response = openai.ChatCompletion.create(
engine=model,
messages=messages,
temperature=0.7,
max_tokens=1000
) | Can you provide me with a Python function that interacts with the OpenAI model, including the necessary code to format and send the request to the model? |
# Build the messages array
messages =[
{"role": "system", "content": system_message},
{"role": "user", "content": user_message},
]
# Call the Azure OpenAI model
response = openai.ChatCompletion.create(
engine=model,
messages=messages,
temperature=0.7,
max_tokens=1000
) | How can I write a Python function that communicates with the OpenAI model, including the code to properly format and dispatch the request to the model? |
# client options same for all services
client_options = {"api_endpoint": API_ENDPOINT}
def create_job_client():
client = vertex_ai_v1beta1.JobServiceClient(client_options=client_options)
return client
def create_model_client():
client = vertex_ai_v1beta1.ModelServiceClient(client_options=client_options)... | Write a Python function to initialize clients from the Vertex AI client library for various services such as job service, model service, endpoint service, and prediction service. |
# client options same for all services
client_options = {"api_endpoint": API_ENDPOINT}
def create_job_client():
client = vertex_ai_v1beta1.JobServiceClient(client_options=client_options)
return client
def create_model_client():
client = vertex_ai_v1beta1.ModelServiceClient(client_options=client_options)... | How can I set up clients for job service, model service, endpoint service, and prediction service using the Vertex AI client library in Python? |
import csv
import os
import openai
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.vectorstores import FAISS
from langchain.vectorstores.base import Document
def read_csv_into_vector_document... | Can you provide me with a Python script that allows me to create my own chatbot using Langchain and OpenAI? |
import csv
import os
import openai
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.vectorstores import FAISS
from langchain.vectorstores.base import Document
def read_csv_into_vector_document... | How can I write a Python code to build a chatbot using Langchain and OpenAI? |
DIMENSIONS = embedding_model.output.shape[1]
DATASET_FILE_PATH = f"{BUCKET_URI}/{dataset_file}"
NEAREST_NEIGHBOR_SEARCH_CONFIG = {
"contentsDeltaUri": "",
"config": {
"dimensions": DIMENSIONS,
"approximateNeighborsCount": 10,
"distanceMeasureType": "SQUARED_L2_DISTANCE",
"featur... | Can you provide me with a Python code snippet that demonstrates how to generate example-based explanations for predictions in a Vertex model using the Vertex Explainable Example-based API? |
DIMENSIONS = embedding_model.output.shape[1]
DATASET_FILE_PATH = f"{BUCKET_URI}/{dataset_file}"
NEAREST_NEIGHBOR_SEARCH_CONFIG = {
"contentsDeltaUri": "",
"config": {
"dimensions": DIMENSIONS,
"approximateNeighborsCount": 10,
"distanceMeasureType": "SQUARED_L2_DISTANCE",
"featur... | How can I use Python to generate example-based explanations when making predictions in a Vertex model, utilizing the Vertex Explainable Example-based API? |
from google.cloud import aiplatform_v1beta1
def create_batch_prediction_job_tabular_forecasting_sample(
project: str,
display_name: str,
model_name: str,
gcs_source_uri: str,
gcs_destination_output_uri_prefix: str,
predictions_format: str,
location: str = "us-central1",
api_endpoint: s... | Can you provide a Python script that creates a batch prediction job for tabular forecasting using Google's Vertex AI platform? |
from google.cloud import aiplatform_v1beta1
def create_batch_prediction_job_tabular_forecasting_sample(
project: str,
display_name: str,
model_name: str,
gcs_source_uri: str,
gcs_destination_output_uri_prefix: str,
predictions_format: str,
location: str = "us-central1",
api_endpoint: s... | How can I use Python to set up a batch prediction job for tabular forecasting with Vertex AI? |
from vertexai.language_models import CodeGenerationModel
def complete_code_function(temperature: float = 0.2) -> object:
"""Example of using Codey for Code Completion to complete a function."""
# TODO developer - override these parameters as needed:
parameters = {
"temperature": temperature, # T... | Can you provide me with a Python code snippet that uses Vertex AI for code comment completion in a generative AI context? |
from vertexai.language_models import CodeGenerationModel
def complete_code_function(temperature: float = 0.2) -> object:
"""Example of using Codey for Code Completion to complete a function."""
# TODO developer - override these parameters as needed:
parameters = {
"temperature": temperature, # T... | How can I use Vertex AI to create prompts for code comment completion in a generative AI setting using Python? |
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
def predict_tabular_regression_sample(
project: str,
endpoint_id: str,
instance_dict: Dict,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis... | Can you provide me with a Python code snippet that uses Vertex AI to predict regression models with tabular data? |
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
def predict_tabular_regression_sample(
project: str,
endpoint_id: str,
instance_dict: Dict,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis... | How can I use Vertex AI in Python to make predictions with regression models on tabular data? |
!pip install -q openai
# importing openai module into your openai environment
import openai
# assigning API KEY to initialize openai environment
openai.api_key = '<API_KEY>
def comp(PROMPT, MaxToken=50, outputs=3):
# using OpenAI's Completion module that helps execute
# any tasks involving text
r... | Can you provide me with a Python code that uses the Completions module of the OpenAI API for translation purposes? |
!pip install -q openai
# importing openai module into your openai environment
import openai
# assigning API KEY to initialize openai environment
openai.api_key = '<API_KEY>
def comp(PROMPT, MaxToken=50, outputs=3):
# using OpenAI's Completion module that helps execute
# any tasks involving text
r... | How can I use the Completions module of the OpenAI API in Python for translating text? |
# import dependencies
from ultralytics import YOLO
import cv2
# load a pretrained model
model = YOLO("yolov8n.pt")
# read the image
img = cv2.imread("image1.jpg")
# inferance the model
results = model.predict(source=img,conf=0.25 , classes=0, save=False)
boxes = results[0].boxes.xyxy.cpu().numpy().astype(int)
# l... | Write a python script to detect only people in a given image using yolov8 and draw bounding boxes in green color on detected people. Finally save the image |
# import dependencies
import cv2
from ultralytics import YOLO
import time
# load a pretrained model
model = YOLO('yolov8n.pt')
pTime = 0
frame_no = 0
video_path = "car_vid.mp4"
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
# read the each frame
success, frame = cap.read()
if success... | write a python code to count the cars in a video footage using yolov8 in each frame and then
draw the bounding boxes and also draw the car count , FPS , frame number on the image in top left corner.
and finally display the video output |
# import dependencies
from ultralytics import YOLO
# load a pretrained model
model = YOLO('yolov8n.pt')
"""
---------safty_dataset folder structure and content of the data.yaml should be ------
safty_dataset
- test
-images
-labels
- train
-images
... | Write a python script to train the yolov8-nano model customly
for the safety equipment data set |
# import dependencies
from ultralytics import YOLO
# load the custom model
model = YOLO('path//to/custom/trained/model/best.pt')
"""
--------- safty_dataset folder structure and content of the data.yaml should be ------
safty_dataset
- test
-images
-labels
- train
... | write a python code snipt to validate the yolov8 custom trained model and then print the MAP valuews |
# import dependencies
import cv2
from ultralytics import YOLO
# load the custom model
model = YOLO('yolov8n.pt')
video_path = "car_vid.mp4"
cap = cv2.VideoCapture(video_path)
# get the frame_width and frame_height
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIG... | write a python code snipt to track only the cars using botsort tracking algorithum in a given video footage and then
draw only the Bounding boxxes and track-ids on the each detected cars and save the video |
# import dependencies
import cv2
from ultralytics import YOLO
# load the custom model
model = YOLO('yolov8n.pt')
video_path = "car_vid.mp4"
cap = cv2.VideoCapture(video_path)
car_count= set()
while cap.isOpened():
# read the each video frame
success, frame = cap.read()
if success:
... | write a python code snipt to count the cars using bytetrack tracking algorithum in a given video footage and then
print the total car count of the video at the end of the code |
# clone the github repo
!git clone https://github.com/ultralytics/yolov5
%cd yolov5
# install the packages
!pip install -qr requirements.txt comet_ml # install the packages
"""
here is the available sourse options
--source 0 # webcam
img.jpg # image
vid.mp4 # video
... | write CLI commands to detect objects using yolov5 |
# clone the github repo
!git clone https://github.com/ultralytics/yolov5
%cd yolov5
# install the packages
!pip install -qr requirements.txt comet_ml # install the packages
# Download COCO val
torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 imag... | write CLI commads to to validate the yolov5 pretained model by using coco val data set |
# clone the github repo
!git clone https://github.com/ultralytics/yolov5
%cd yolov5
# install the packages
!pip install -qr requirements.txt comet_ml # install the packages
"""
* Train a YOLOv5s model on the COCO128 dataset with --data coco128.yaml,
starting from pretrained --weights... | write CLI commads to train the yolov5 model customly for coco128 dataset |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.yaml') # build a new model from YAML
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n-cls.yaml').load('yolov8n-cls.pt') # build from YAML and transfer weights
# Train t... | write a code snippet to train YOLOv8n-cls on the MNIST160 dataset for 100 epochs at image size 64 |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.top1 # top1 accuracy
metrics.top5... | write code snipts to validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
# Or either you can use CLI
yolo c... | generate a code to use a trained YOLOv8n-cls model to run predictions on images |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.yaml') # build a new model from YAML
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
# Train the model
results... | write a python code sniptto train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640 |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
# Or either you can use CLI
yolo detec... | write a python code snipt to Use a trained YOLOv8n model to run predictions on images. |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom trained model
# Export the model
model.export(format='onnx')
# Or either you can use CLI
yolo export model=yolov8n.pt format=onnx # export official m... | write a python snipit to export a YOLOv8n model to a different format like ONNX |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.yaml') # build a new model from YAML
model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n-seg.yaml').load('yolov8n.pt') # build from YAML and transfer weights
# Train the m... | write a python code to train YOLOv8n-seg on the COCO128-seg dataset for 100 epochs at image size 640. |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.box.map # map50-95(B)
metrics.bo... | write a python code to validate trained YOLOv8n-seg model accuracy on the COCO128-seg dataset |
# import dependencies
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
# Or either you can use CLI
yolo s... | write a python code to Use a trained YOLOv8n-seg model to run predictions on images. |
pip install openai
import os
import openai
import requests
import json
openai.api_key = "xxxxxxxxxxxxxxxxxx"
url = "https://api.openai.com/v1/chat/completions"
payload = json.dumps({
"model": "gpt-4-0613",
"messages": [
{
"role": "user",
"content": "Send Cobus from HumanFirst AI an email and ask ... | Write me python code to compile emails with a few fields defined, which need to be populated by calling Open AI API |
# Build the messages array
messages =[
{"role": "system", "content": system_message},
{"role": "user", "content": user_message},
]
# Call the Azure OpenAI model
response = openai.ChatCompletion.create(
engine=model,
messages=messages,
temperature=0.7,
max_tokens=1000
) | Write me python function that calls the OpenAI model, add the code to format and send the request to the model. |
# client options same for all services
client_options = {"api_endpoint": API_ENDPOINT}
def create_job_client():
client = vertex_ai_v1beta1.JobServiceClient(client_options=client_options)
return client
def create_model_client():
client = vertex_ai_v1beta1.ModelServiceClient(client_options=client_options)... | Write me python function to setup clients from vertex AI client library for services like job service, model service, endpoint service and prediction service |
import csv
import os
import openai
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.vectorstores import FAISS
from langchain.vectorstores.base import Document
def read_csv_into_vector_document... | write me python code to create own chatbot using Langchain and OpenAI. |
DIMENSIONS = embedding_model.output.shape[1]
DATASET_FILE_PATH = f"{BUCKET_URI}/{dataset_file}"
NEAREST_NEIGHBOR_SEARCH_CONFIG = {
"contentsDeltaUri": "",
"config": {
"dimensions": DIMENSIONS,
"approximateNeighborsCount": 10,
"distanceMeasureType": "SQUARED_L2_DISTANCE",
"featur... | Write me python code to generate example-based explanation when doing a prediction in vertex model using vertex Explainable Example-based API |
from google.cloud import aiplatform_v1beta1
def create_batch_prediction_job_tabular_forecasting_sample(
project: str,
display_name: str,
model_name: str,
gcs_source_uri: str,
gcs_destination_output_uri_prefix: str,
predictions_format: str,
location: str = "us-central1",
api_endpoint: s... | Write me a python code to Create a batch prediction job for tabular forecasting using vertex AI |
from vertexai.language_models import CodeGenerationModel
def complete_code_function(temperature: float = 0.2) -> object:
"""Example of using Codey for Code Completion to complete a function."""
# TODO developer - override these parameters as needed:
parameters = {
"temperature": temperature, # T... | Write me python code to Create prompts for code comment completion (Generative AI) use vertex ai |
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
def predict_tabular_regression_sample(
project: str,
endpoint_id: str,
instance_dict: Dict,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis... | Write me python code to predict regression models with tabular data using vertex ai |
!pip install -q openai
# importing openai module into your openai environment
import openai
# assigning API KEY to initialize openai environment
openai.api_key = '<API_KEY>
def comp(PROMPT, MaxToken=50, outputs=3):
# using OpenAI's Completion module that helps execute
# any tasks involving text
r... | Write me a python code to Translation using Completions module of OpenAI API
|
In python, langchain is a library used to build application with large language models | What is langchain? |
In langchain, Large language model are chat models. Large Language Models can be alternatively called as LLM or chat models | What is a Large Language Model? |
In langchain, prompt is message that can be sent to chat model as a part of a conversation. Prompts can alternatively be referred as messages to chat model | What is a prompt in langchain? |
pip install langchain | Langchain is a library in python that can be used for developing application using large language models. It can be installed using 'pip' command .For example: pip install langchain |
#chat models availalble from openAI can be imported using ChatOpenAI
from langchain.chat_models import ChatOpenAI | chat models are type of large language models where input and output are 'chat messages'. Langchain provides a convenient way to import these chat models. For example, to import chat models available from a company called openai, we can use the command: from langchain.chat_models import ChatOpenAI |
#import ChatOpenAI class to use chat models from langchain library
from langchain.chat_models import ChatOpenAI
#initialize a chat model from from ChatOpenAI
#initialize and import a chat model using ChatOpenAI()
llm = ChatOpenAI(model_name="name of the model goes here") | in Langchain we can import chat models available from openAI through 'ChatOpenAI' class. Once the 'ChatOpenAI' class is imported, we can initialize it. For example:from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model_name="insert model name here") |
#import ChatOpenAI class to use chat models from langchain library
from langchain.chat_models import ChatOpenAI
#set model_name as gpt-3.5-turbo model in ChatOpenAI()
llm = ChatOpenAI(model_name="gpt-3.5-turbo") | gpt-3.5-turbo developed by openAI company is a well established large language model that can be used for building chat bots using langchain framework in python. model_name parameter can be passed to the ChatOpenAI() to initiaze a model.We can pass model_name='gpt-3.5-turbo' as a parameter to ChatOpenAI() to initialize... |
#import ChatOpenAI class to use chat models from langchain library
from langchain.chat_models import ChatOpenAI
#set model_name and temperature parmater in ChatOpenAI()
#for creative response set temperature=1.0
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=1.0) | temperature parameter in ChatOpenAI(model="gpt-3.5-turbo",temperature) can be used to adjust the creative response of the model. To get a creative response from chat model, we can set 'temperature=1.0' .For example: from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model_name="gpt-3.5-turbo",temperature=1.... |
#import ChatOpenAI class to use chat models from langchain library
from langchain.chat_models import ChatOpenAI
#set model_name and temperature parmater in ChatOpenAI()
#For less creative and factual response set temperature=0.1
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.1) | temperature parameter in ChatOpenAI(model="gpt-3.5-turbo",temperature) can be used to adjust the creative response of the model. To get a factual response where creativity is not needed from chat model, we can set 'temperature=0.1'.For example: from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model_name="... |
#import chat prompt template from langchain
from langchain.prompts import ChatPromptTemplate | In langchain, Chat prompt template can be used to pass list of prompts to the chat model. It is available through 'lanchain.prompts'. For example: from langchain.prompts import ChatPromptTemplate |
#import ChatPromptTemplate to pass list of prompts to the chat model
from langchain.prompts import ChatPromptTemplate | In langchain, ChatPromptTemplate can be used to pass list of prompts to the model. It can be imported from 'lanchain.prompts' library.For example: from langchain.prompts import ChatPromptTemplate |
#import SystemMessage from langchain
from langchain.prompts.chat import SystemMessage | In langchain, 'SystemMessage' can be use to pass a prompt/message that sets the role and objectives that chat model should follow. 'SystemMessage' can be imported from 'langchain.prompts'. For example: from langchain.prompts.chat import SystemMessage |
#import chat prompt template from langchain
from langchain.prompts import ChatPromptTemplate
#import SystemMessage from langchain to set the role and objectives for the chat model
from langchain.prompts.chat import SystemMessage | In langchain, 'SystemMessage' can be used to pass a prompt/message that sets the role and object for the chat model. We can pass the 'SystemMessage' prompt throught 'ChatPromptTemplate'. We can import the necessary libraries as follows: from langchain.prompts import ChatPromptTemplate
from langchain.prompts.chat import... |
#import HumanMessage to pass message of the user to chat model
from langchain.prompts.chat import HumanMessage | In langchain, 'HumanMessage' is a prompt/message that is sent from the perspective of human. It mostly includes question to the chat models. 'HumanMessage' can be imported from 'langchain.prompts' library. For example: from langchain.prompts.chat import HumanMessage |
#import ChatOpenAI to begin chatting with a chat model
from langchain.chat_models import ChatOpenAI
#import ChatPromptTemplate to send prompts to the chat model
from langchain.prompts import ChatPromptTemplate
#import SystemMessage to create a prompt that sets objective for the chat model
from langchain.schema.mes... | In langchain, we can pass a prompt using 'SystemMessage' to 'ChatPromptTemplate' to start chatting with a chat model. 'SystemMessage' is used to pass a prompt for setting a role and objective for the chat model and for instance one can ask the chat model to behave as a language tranlator. To set a prompt as 'SystemMess... |
#import ChatOpenAI to begin chatting with a chat model
from langchain.chat_models import ChatOpenAI
#import ChatPromptTemplate to send prompts to the chat model
from langchain.prompts import ChatPromptTemplate
#import SystemMessage to create a prompt that sets objective for the chat model
from langchain.schema.mes... | In langchain, 'HumanMessage' message can be used by the user to ask a question to the chat model. Similar to 'SystemMessage', we can set a python string to 'content' parameter inside 'HumanMessage()'.For example: from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from lang... |
#import ChatOpenAI to begin chatting with a chat model
from langchain.chat_models import ChatOpenAI
#import ChatPromptTemplate to send prompts to the chat model
from langchain.prompts import ChatPromptTemplate
#import SystemMessage to create a prompt that sets objective for the chat model
from langchain.schema.mes... | In langchain, prompts set using 'SystemMessage' and 'HumanMessage' are passed through 'ChatPromptTemplate'. 'ChatPromptTemplate' has a method called 'from_messages()' through which we can assemble the prompts. For example: from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
... |
#import ChatOpenAI to begin chatting with a chat model
from langchain.chat_models import ChatOpenAI
#import ChatPromptTemplate to send prompts to the chat model
from langchain.prompts import ChatPromptTemplate
#import SystemMessage to create a prompt that sets objective for the chat model
from langchain.schema.mes... | In langchain, prompt templates can contain place holders like {{variable_name}}.format_messages() allows us to substitute these variables later. Since variables are used, it makes the prompt flexible. If variable is used in 'HumanMessage' and substitute it using format_messages, it gives the user the flexibility to ask... |
#import ChatOpenAI to begin chatting with a chat model
from langchain.chat_models import ChatOpenAI
#import ChatPromptTemplate to send prompts to the chat model
from langchain.prompts import ChatPromptTemplate
#import SystemMessage to create a prompt that sets objective for the chat model
from langchain.schema.mes... | In langchain, we can use 'ChatPromptTemplate' to assemble prompts from 'SystemMessage' and 'HumanMessage' and pass the prompts to a chat model and then the chat model will output a response. For example:from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.sche... |
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