Hackster is hosting Hackster Holidays, Ep. 6: Livestream & Giveaway Drawing. Watch previous episodes or stream live on Monday!Stream Hackster Holidays, Ep. 6 on Monday!
Kutluhan Aktar
Published © CC BY

AI-driven Interactive Lab Assistant w/ OpenCV & ChatGPT

In remote learning, provide insightful guidance on lab equipment as auto-generated lessons via object detection and artificial intelligence.

ExpertFull instructions provided3,014

Things used in this project

Hardware components

NVIDIA Jetson Nano Developer Kit
NVIDIA Jetson Nano Developer Kit
×1
Elecrow CrowVision 11.6'' TouchScreen Module (1366x768)
×1
USB Webcam (PK-910H)
×1
Anycubic Kobra 2 Max
×1
SparkFun WaveShare 8Ω 5W Speakers
×1

Software apps and online services

Edge Impulse Studio
Edge Impulse Studio
OpenCV
OpenCV
OpenAI API (ChatGPT)
Visual Studio 2017
Microsoft Visual Studio 2017
Fusion
Autodesk Fusion
Ultimaker Cura

Hand tools and fabrication machines

Hot glue gun (generic)
Hot glue gun (generic)

Story

Read more

Custom parts and enclosures

Edge Impulse Model (Linux AARCH64)

AIoT_Lab_Assistant_main_case.stl

AIoT_Lab_Assistant_top_cover.stl

AIoT_Lab_Assistant_support_legs.stl

AIoT_Lab_Assistant_pin_1.stl

AIoT_Lab_Assistant_pin_2.stl

AIoT_Lab_Assistant_pin_3.stl

AIoT_Lab_Assistant_pin_4.stl

Schematics

CrowVision

Code

_class.py

Python
# AI-driven Interactive Lab Assistant w/ OpenCV & ChatGPT
#
# NVIDIA Jetson Nano
#
# By Kutluhan Aktar
#
# In remote learning, provide insightful guidance on lab equipment as auto-generated lessons
# via object detection and artificial intelligence. 
# 
#
# For more information:
# https://www.hackster.io/kutluhan-aktar

import cv2
import numpy
from guizero import App, Window, Box, Text, TextBox, PushButton, ButtonGroup, Combo, CheckBox, MenuBar, Picture, info, yesno, warn
from gtts import gTTS
from edge_impulse_linux.image import ImageImpulseRunner
import os
import requests
import subprocess
import webbrowser
import datetime
from time import sleep


class lab_assistant_op():
    def __init__(self, model_file):
        # Initialize the USB high-quality camera feed.
        self.camera = cv2.VideoCapture(0)
        sleep(3)
        # Define the required variables to configure camera settings.
        self.con_opt = "normal"
        self.cam_init = True
        self.frame_size = (480,480)
        # Define the required configurations to run the Edge Impulse FOMO object detection model.
        dir_path = os.path.dirname(os.path.realpath(__file__))
        self.model_file = os.path.join(dir_path, model_file)
        self.detected_class = "Waiting..."
        # Define the required variables for the OpenAI API.
        self.OPENAI_API_KEY = "OPENAI_API_KEY"
        self.OPENAI_ENDPOINT = "https://api.openai.com/v1/chat/completions"
        # Assign the defined user interface structure.
        self.create_user_interface()
        sleep(10)

    def run_inference(self, notify=False):
        # Run inference to detect various types of lab equipment.
        with ImageImpulseRunner(self.model_file) as runner:
            try:
                # Print the information of the Edge Impulse model converted to a Linux (AARCH64) application (.eim).
                model_info = runner.init()
                print('Loaded runner for "' + model_info['project']['owner'] + ' / ' + model_info['project']['name'] + '"')
                labels = model_info['model_parameters']['labels']
                # Get the currently captured image with the high-quality USB camera.
                # Then, convert the any given frame format to RGB by temporarily saving the frame as a JPG file and reading it.
                cv2.imwrite("./tmp/temp_frame.jpg", self.latest_frame)
                # After reading the temporary JPG file, resize the converted frame depending on the given model so as to run an inference. 
                test_img = cv2.imread("tmp/temp_frame.jpg")
                features, cropped = runner.get_features_from_image(test_img)
                res = runner.classify(features)
                # Obtain the prediction (detection) results for each label (class).
                if "bounding_boxes" in res["result"].keys():
                    print('Found %d bounding boxes (%d ms.)' % (len(res["result"]["bounding_boxes"]), res['timing']['dsp'] + res['timing']['classification']))
                    # If the Edge Impulse model predicts a class successfully:
                    if(len(res["result"]["bounding_boxes"]) == 0):
                        self.detected_class = "[...]"
                    else:
                        for bb in res["result"]["bounding_boxes"]:
                            # Get the latest detected labels:
                            self.detected_class = bb['label']
                            print('\t%s (%.2f): x=%d y=%d w=%d h=%d' % (bb['label'], bb['value'], bb['x'], bb['y'], bb['width'], bb['height']))
                            cropped = cv2.rectangle(cropped, (bb['x'], bb['y']), (bb['x'] + bb['width'], bb['y'] + bb['height']), (255, 0, 0), 1)
                # Show the model detection image with the passed bounding boxes if any on the screen.
                cv2.imshow("Model Detection Image", cropped)
                # Remove the temporary image file.
                os.remove("./tmp/temp_frame.jpg")
                # Update the interface inquiry options according to the predicted class (label).
                __l = self.detected_class.replace("_", " ")
                self.inquiry_input.clear()
                self.inquiry_input.insert(0, "How to use {} in labs?".format(__l))
                self.inquiry_input.insert(1, "Please list the lab experiments with {}".format(__l))
                self.inquiry_input.insert(2, "Tell me about recent research papers on {}".format(__l))
                self.inquiry_input.insert(3, "How to repair {}?".format(__l))
                # Notify the user of the detection results.
                self.model_notify.value = "Latest Detection: " + self.detected_class
                print("\n\nLatest Detected Label => " + self.detected_class)
                # If requested, also inform the user via a pop-up window on the screen.
                if notify:
                    self.app.warn("Latest Detected Label", self.detected_class)        
            # Stop the running inference.    
            finally:
                if(runner):
                    runner.stop()

    def chatgpt_get_information(self, inquiry):
        # Make a cURL call (request) to the OpenAI API in order to get information regarding the given lab component from ChatGPT.
        # Define the required HTML headers.
        headers = {"Authorization": "Bearer " + self.OPENAI_API_KEY, "Content-Type": "application/json"}
        # Define POST data parameters in the JSON format.
        json_data = {"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": inquiry}, {"role": "user", "content": "Please add the latest asked question before the answer."}], "temperature": 0.6}
        # Obtain information from ChatGPT by making a cURL call to the OpenAI API.
        curl = requests.post(self.OPENAI_ENDPOINT, json=json_data, headers=headers)
        if(curl.status_code == 200):
            chatgpt_info = curl.json()
            chatgpt_info = chatgpt_info["choices"][0]["message"]["content"]
            return chatgpt_info
        else:
            return "Error: ChatGPT"
    
    def chatgpt_show_information(self):
        selected_inquiry = self.inquiry_input.value
        # Notify the user of the passed inquiry.
        user_notification = "Inquiry: " + selected_inquiry + "\n\nWaiting response from ChatGPT..."
        info("Status", user_notification)
        # Obtain information from ChatGPT.
        chatgpt_info = self.chatgpt_get_information(selected_inquiry)
        print("ChatGPT Response Received Successfully!")
        # Display the received information generated by ChatGPT on the second window.
        self.second_window.show()
        self.second_w_text.value = chatgpt_info
        # If requested, convert the received ChatGPT response to audio lesson and save it.
        audio_lesson_opt = yesno("Audio Lesson", "Save the obtained ChatGPT response as audio lesson?")
        if (audio_lesson_opt == True):
            print("\n\nConverting ChatGPT response...")
            self.text_to_speech_lesson(chatgpt_info)

    def text_to_speech_lesson(self, _text, language="en"):
        # Convert the information about the given lab equipment, generated by ChatGPT, to speech (audio lesson).
        text_to_speech = gTTS(text=_text, lang=language, slow=False)
        # Define the audio file (MP3) path, including the current date.
        date = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        file_name = self.detected_class + "_" + date + ".mp3"
        file_path = "audio_lesson/" + file_name
        # Save the generated speech (audio) file.
        text_to_speech.save(file_path)
        sleep(2)
        print("\nAudio Lesson Saved: " + file_name)
        # If requested, play the generated audio lesson immediately.
        if(self.audio_input.value == True):
            print("\nLatest Audio Lesson: Playing...")
            os.system("mpg123 " + file_path)

    def display_camera_feed(self, threshold1=120, threshold2=170, iterations=1):
        if(self.cam_init == True):
            # Display the real-time video stream generated by the USB camera.
            ret, img = self.camera.read()
            # Resize the captured frame depending on the given object detection model.
            mod_img = cv2.resize(img, self.frame_size)
            # Define the structuring element used for canny edge modifications.
            kernel = numpy.ones((4,4), numpy.uint8)
            # Apply the given image conversion option to the resized frame.
            if(self.con_opt == "normal"):
                self.latest_frame = mod_img
            if(self.con_opt == "gray"):
                self.latest_frame = cv2.cvtColor(mod_img, cv2.COLOR_BGR2GRAY)
            if(self.con_opt =="blur"):
                self.latest_frame = cv2.GaussianBlur(mod_img, (11,11), 0)
            if(self.con_opt =="canny"):
                self.latest_frame = cv2.Canny(mod_img, threshold1, threshold2)
            if(self.con_opt =="canny+"):
                canny_img = cv2.Canny(mod_img, threshold1, threshold2)
                self.latest_frame = cv2.dilate(canny_img, kernel, iterations=iterations)
            if(self.con_opt =="canny++"):
                canny_img = cv2.Canny(mod_img, threshold1, threshold2)
                self.latest_frame = cv2.dilate(canny_img, kernel, iterations=iterations+1)
            if(self.con_opt =="canny-"):
                canny_img = cv2.Canny(mod_img, threshold1, threshold2)
                canny_img = cv2.dilate(canny_img, kernel, iterations=iterations)
                self.latest_frame = cv2.erode(canny_img, kernel, iterations=iterations)
            if(self.con_opt =="canny--"):
                canny_img = cv2.Canny(mod_img, threshold1, threshold2)
                canny_img = cv2.dilate(canny_img, kernel, iterations=iterations)
                self.latest_frame = cv2.erode(canny_img, kernel, iterations=iterations+1)
            # Show the modified frame on the screen.
            cv2.imshow("Lab Assistant Camera Feed", self.latest_frame)
        else:
            s_img = cv2.imread("assets/camera_stopped.png")
            s_img = cv2.resize(s_img, self.frame_size)
            cv2.imshow("Lab Assistant Camera Feed", s_img)
        # Stop the camera feed if requested.
        if cv2.waitKey(1) & 0xFF == ord('q'):
            self.camera.release()
            cv2.destroyAllWindows()
            print("\nCamera Feed Stopped!")
    
    def save_img_sample(self):
        # Create the file name for the image sample.
        date = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        given_class = self.col_buttons_input.value.strip()
        filename = "./samples/IMG_{}_{}.jpg".format(given_class, date)
        # If requested, save the recently captured image (latest frame) with the applied conversion as a sample.
        cv2.imwrite(filename, self.latest_frame)
        # Notify the user after saving the sample.
        self.col_save_notify.value = "Latest Label: " + given_class
        print("\nSample Saved Successfully: " + filename)

    def change_img_conv(self, con_opt):
        # Change the image conversion option.
        self.con_opt = con_opt
        print("\nCamera Image Conversion ==> " + con_opt)
    
    def camera_stop(self, action):
        # Stop or resume the real-time camera feed.
        if(action == True):
            self.cam_init = False
            print("\nCamera Feed Stopped!")
            sleep(1)
        else:
            self.cam_init = True
            print("\nCamera Feed Resume!")
            sleep(1) 
    
    def camera_feed(self):
        # Start the camera feed loop.
        while True:
            self.display_camera_feed()

    def create_user_interface(self, appWidth=900, appHeight=600, b_set=["#FFE681", "#BA0C2E", 12, [6,10], "#BA0C2E"], _cursor="hand1"):
        # Design the user interface (GUI) via the guizero module.
        # And, disable the app window resizing to provide a better user experience.
        self.app = App(title="AI-driven Lab Assistant w/ ChatGPT", bg="#5C5B57", width=appWidth, height=appHeight)
        self.app.font = "Comic Sans MS" 
        self.app.tk.resizable(False, False)
        menubar = MenuBar(self.app, toplevel=["Edge Impulse Model", "Previous Lessons", "Help"], options=[[["Go to public model page", lambda:self.menu_com("inspect_m")], ["Inspect Enterprise Features", lambda:self.menu_com("inspect_e")]], [["Inspect", lambda:self.menu_com("pre_lesson")]], [["Project Tutorial", lambda:self.menu_com("o_tutorial")], ["ChatGPT", lambda:self.menu_com("h_chatgpt")]]])
        menubar.bg="#F9E5C9"
        menubar.tk.config(bg="#F9E5C9", fg="#5C5B57", activebackground="#F5F5F0", activeforeground="#5C5B57", cursor="plus")
        # Layout.
        app_header = Box(self.app, width=appWidth, height=50, align="top")
        app_header_text = Text(app_header, text="   Data Collection", color="white", size=20, align="left")
        app_header_text.tk.config(cursor="sb_down_arrow")
        app_header_text = Text(app_header, text=" Generate Lesson  ", color="white", size=20, align="right")
        app_header_text.tk.config(cursor="sb_down_arrow")
        app_data_collect = Box(self.app, width=appWidth/2, height=appHeight-50, layout="grid", align="left")
        app_run_model = Box(self.app, width=appWidth/2, height=appHeight-50, layout="grid", align="right")
        app_run_model.bg = "#215E7C"
        # User frame conversion configurations.
        conv_buttons = Text(app_data_collect, text="Conversions: ", color="#FFE681", size=14, grid=[0,0], align="left")
        conv_buttons_con = Box(app_data_collect, grid=[1,0], layout="auto", width="fill", height="fill", align="left")
        conv_buttons_con.tk.config(pady=8, padx=15)
        conv_buttons_con_1 = Box(conv_buttons_con, layout="auto", width="fill", height="fill", align="top")
        conv_buttons_con_1.tk.config(pady=4)
        conv_buttons_con_2 = Box(conv_buttons_con, layout="auto", width="fill", height="fill", align="top")
        conv_buttons_con_2.tk.config(pady=4)
        conv_buttons_con_3 = Box(conv_buttons_con, layout="auto", width="fill", height="fill", align="top")
        conv_buttons_con_3.tk.config(pady=4)
        conv_buttons = PushButton(conv_buttons_con_1, text="Normal", align="left", command=lambda:self.change_img_conv("normal"), padx=b_set[3][0], pady=b_set[3][1])
        conv_buttons.bg = b_set[0]
        conv_buttons.text_color = b_set[1]
        conv_buttons.text_size = b_set[2]
        conv_buttons.tk.config(highlightthickness=2, highlightbackground=b_set[4], highlightcolor=b_set[4], cursor=_cursor)
        conv_buttons = PushButton(conv_buttons_con_1, text="Gray", align="left", command=lambda:self.change_img_conv("gray"), padx=b_set[3][0], pady=b_set[3][1])
        conv_buttons.bg = b_set[0]
        conv_buttons.text_color = b_set[1]
        conv_buttons.text_size = b_set[2]
        conv_buttons.tk.config(highlightthickness=2, highlightbackground=b_set[4], highlightcolor=b_set[4], cursor=_cursor)
        conv_buttons = PushButton(conv_buttons_con_1, text="Blur", align="left", command=lambda:self.change_img_conv("blur"), padx=b_set[3][0], pady=b_set[3][1])
        conv_buttons.bg = b_set[0]
        conv_buttons.text_color = b_set[1]
        conv_buttons.text_size = b_set[2]
        conv_buttons.tk.config(highlightthickness=2, highlightbackground=b_set[4], highlightcolor=b_set[4], cursor=_cursor)
        conv_buttons = PushButton(conv_buttons_con_1, text="Canny", align="left", command=lambda:self.change_img_conv("canny"), padx=b_set[3][0], pady=b_set[3][1])
        conv_buttons.bg = b_set[0]
        conv_buttons.text_color = b_set[1]
        conv_buttons.text_size = b_set[2]
        conv_buttons.tk.config(highlightthickness=2, highlightbackground=b_set[4], highlightcolor=b_set[4], cursor=_cursor)
        conv_buttons = PushButton(conv_buttons_con_2, text="Canny+", align="left", command=lambda:self.change_img_conv("canny+"), padx=b_set[3][0], pady=b_set[3][1])
        conv_buttons.bg = b_set[0]
        conv_buttons.text_color = b_set[1]
        conv_buttons.text_size = b_set[2]
        conv_buttons.tk.config(highlightthickness=2, highlightbackground=b_set[4], highlightcolor=b_set[4], cursor=_cursor)
        conv_buttons = PushButton(conv_buttons_con_2, text="Canny++", align="left", command=lambda:self.change_img_conv("canny++"), padx=b_set[3][0], pady=b_set[3][1])
        conv_buttons.bg = b_set[0]
        conv_buttons.text_color = b_set[1]
        conv_buttons.text_size = b_set[2]
        conv_buttons.tk.config(highlightthickness=2, highlightbackground=b_set[4], highlightcolor=b_set[4], cursor=_cursor)
        conv_buttons = PushButton(conv_buttons_con_3, text="Canny-", align="left", command=lambda:self.change_img_conv("canny-"), padx=b_set[3][0], pady=b_set[3][1])
        conv_buttons.bg = b_set[0]
        conv_buttons.text_color = b_set[1]
        conv_buttons.text_size = b_set[2]
        conv_buttons.tk.config(highlightthickness=2, highlightbackground=b_set[4], highlightcolor=b_set[4], cursor=_cursor)
        conv_buttons = PushButton(conv_buttons_con_3, text="Canny--", align="left", command=lambda:self.change_img_conv("canny--"), padx=b_set[3][0], pady=b_set[3][1])
        conv_buttons.bg = b_set[0]
        conv_buttons.text_color = b_set[1]
        conv_buttons.text_size = b_set[2]
        conv_buttons.tk.config(highlightthickness=2, highlightbackground=b_set[4], highlightcolor=b_set[4], cursor=_cursor)
        # User camera configurations.
        cam_buttons = Text(app_data_collect, text="Camera Feed: ", color="#FFE681", size=14, grid=[0,2], align="left")
        cam_buttons_con = Box(app_data_collect, grid=[1,2], layout="auto", width="fill", height="fill", align="right")
        cam_buttons_con.bg = "#364935"
        cam_buttons_con.tk.config(padx=15,pady=15)
        cam_buttons_con.set_border(3, "white")
        cam_buttons = PushButton(cam_buttons_con, text="Start", align="left", command=lambda:self.camera_stop(False), padx=b_set[3][0]+15, pady=b_set[3][1])
        cam_buttons.bg = b_set[0]
        cam_buttons.text_color = b_set[1]
        cam_buttons.text_size = b_set[2]
        cam_buttons.tk.config(highlightthickness=2, highlightbackground=b_set[4], highlightcolor=b_set[4], cursor=_cursor)
        cam_buttons = PushButton(cam_buttons_con, text="Stop", align="right", command=lambda:self.camera_stop(True), padx=b_set[3][0]+15, pady=b_set[3][1])
        cam_buttons.bg = b_set[0]
        cam_buttons.text_color = b_set[1]
        cam_buttons.text_size = b_set[2]
        cam_buttons.tk.config(highlightthickness=2, highlightbackground=b_set[4], highlightcolor=b_set[4], cursor=_cursor)
        # Data collection configurations.
        col_buttons = Text(app_data_collect, text="", color="black", size=10, grid=[0,3], align="left")
        col_buttons = Text(app_data_collect, text="Assign Label:", color="#FFE681", size=14, grid=[0,4], align="left")
        col_buttons_con = Box(app_data_collect, grid=[1,4], layout="grid", width="fill", height="fill", align="right")
        col_buttons_con.bg = "#364935"
        col_buttons_con.tk.config(padx=3, pady=10, highlightbackground="white", highlightcolor="white")
        col_buttons_con.set_border(5, "white")
        self.col_buttons_input = TextBox(col_buttons_con, grid=[0,0], width=20, height=3, multiline=True, text="Please enter a label...", scrollbar=False)
        self.col_buttons_input.bg = "#5C5B57"
        self.col_buttons_input.text_size = 15
        self.col_buttons_input.text_color = "white"
        self.col_buttons_input.tk.config(highlightthickness=3, highlightbackground="white", highlightcolor="white")
        col_buttons = Text(col_buttons_con, text="", color="black", size=5, grid=[0,1], align="left")
        col_buttons = PushButton(col_buttons_con, text="Save Sample", grid=[0,2], align="right", command=self.save_img_sample, padx=b_set[3][0]+15, pady=b_set[3][1])
        col_buttons.bg = b_set[1]
        col_buttons.text_color = b_set[0]
        col_buttons.text_size = b_set[2]
        col_buttons.tk.config(highlightthickness=3, highlightbackground=b_set[0], highlightcolor=b_set[0], cursor=_cursor)
        col_buttons = Text(col_buttons_con, text="", color="black", size=5, grid=[0,3], align="left")
        col_buttons = PushButton(col_buttons_con, text="Inspect Samples", grid=[0,4], align="right", command=lambda:self.menu_com("inspect_samples"), padx=b_set[3][0]+15, pady=b_set[3][1])
        col_buttons.bg = b_set[1]
        col_buttons.text_color = b_set[0]
        col_buttons.text_size = b_set[2]
        col_buttons.tk.config(highlightthickness=3, highlightbackground=b_set[0], highlightcolor=b_set[0], cursor=_cursor)
        self.col_save_notify = Text(col_buttons_con, text="Latest Label: Waiting...", color=b_set[0], size=10, grid=[0,6], align="right")
        self.col_save_notify.tk.config(pady=3)
        # Inquiry configurations.
        inquiry_buttons = Text(app_run_model, text="Inquiry:", color="orange", size=14, grid=[0,0], align="left")
        inquiry_buttons_con = Box(app_run_model, grid=[1,0], layout="auto", width=310, height=80, align="right")
        self.inquiry_input = Combo(inquiry_buttons_con, align="right", options=["How to use [...] in labs?", "Please list the lab experiments with [...]", "Tell me about recent research papers on [...]", "How to repair [...]?"])
        self.inquiry_input.bg = "#F9E5C9"
        self.inquiry_input.text_color = "#5C5B57"
        self.inquiry_input.text_size = 8
        self.inquiry_input.tk.config(cursor="question_arrow")
        # Audio player configurations.
        audio_buttons = Text(app_run_model, text="Audio Player: ", color="orange", size=14, grid=[0,1], align="left")
        self.audio_input = CheckBox(app_run_model, text="Activate Instant Play", grid=[1,1], align="right")
        self.audio_input.text_size = 13
        self.audio_input.text_color = "#F9E5C9"
        self.audio_input.tk.config(highlightthickness=0, pady=20, cursor="exchange")
        # Edge Impulse object detection model configurations.
        model_buttons = Text(app_run_model, text="", color="black", size=22, grid=[0,2], align="left")
        list_button = PushButton(app_run_model, grid=[0,3], align="left", command=lambda:self.menu_com("filters"), padx=b_set[3][0], pady=b_set[3][1])
        list_button.image = "assets/lab_icon.png"
        list_button.tk.config(highlightthickness=5, highlightbackground="#F9E5C9", highlightcolor="#F9E5C9", cursor=_cursor)
        model_buttons_con = Box(app_run_model, grid=[1,3], layout="grid", width="fill", height="fill", align="right")
        model_buttons_con.bg = "#9BB5CE"
        model_buttons_con.set_border(5, "#F9E5C9")
        model_buttons_con.tk.config(padx=20, pady=30)
        run_button = PushButton(model_buttons_con, text="Run Inference", grid=[0,0], align="right", command=lambda:self.run_inference(notify=True), padx=b_set[3][0], pady=b_set[3][1], width=19, height=3)
        run_button.bg = "orange"
        run_button.text_color = "#5C5B57"
        run_button.text_size = b_set[2]+2
        run_button.tk.config(highlightthickness=6, highlightbackground="#5C5B57", highlightcolor="#5C5B57", cursor=_cursor)
        self.model_notify = Text(model_buttons_con, text="Latest Detection: " + self.detected_class, color="#5C5B57", size=10, grid=[0,1], align="right")
        model_buttons = Text(model_buttons_con, text="", color="black", size=20, grid=[0,2], align="left")
        model_buttons = PushButton(model_buttons_con, text="Get ChatGPT Response", grid=[0,3], align="right", command=self.chatgpt_show_information, padx=b_set[3][0], pady=b_set[3][1], width=19, height=3)
        model_buttons.bg = "orange"
        model_buttons.text_color = "#5C5B57"
        model_buttons.text_size = b_set[2]+2
        model_buttons.tk.config(highlightthickness=6, highlightbackground="#5C5B57", highlightcolor="#5C5B57", cursor=_cursor)
        # Create the second window to display the information generated by ChatGPT.
        self.second_window = Window(self.app, title="Response from ChatGPT", bg="#5C5B57", width=appWidth, height=appHeight, layout= "grid")
        self.second_window.tk.resizable(False, False)
        self.second_window.hide()
        second_w_logo = Picture(self.second_window, image="assets/chatgpt_logo.png", width=200, height=200, grid=[0,0], align="top")
        second_w_logo.tk.config(padx=15, pady=15, highlightthickness=6, highlightbackground="white", highlightcolor="white")
        second_w_show_con = Box(self.second_window, width="fill", height="fill", grid=[1,0], layout="auto", align="left")
        second_w_show_con.tk.config(padx=25, pady=15)
        self.second_w_text = TextBox(second_w_show_con, width=62, height=29, multiline=True, text="Waiting ChatGPT Response...", scrollbar=False)
        self.second_w_text.bg = "#74AA9C"
        self.second_w_text.text_size = 12
        self.second_w_text.text_color = "white"
        self.second_w_text.tk.config(padx=10, pady=10, highlightthickness=6, highlightbackground="white", highlightcolor="white", cursor="target")
        
    def menu_com(self, com="help"):
        # Define the button commands.
        if(com == "inspect_samples"):
            subprocess.Popen(["xdg-open", "./samples"])
        if(com == "filters"):
            info("EI Model Label Filters", "spoon_spatula => Canny++\n\nforcep => Canny-\n\ndynamometer => Canny+\n\nbunsen_burner => Gray\n\nalcohol_burner => Blur\n\ntest_tube => Canny-\n\nskeleton_model => Canny\n\nmicroscope => Canny\n\nhatchery => Canny\n\nmicroscope_slide => Canny--")
        # Define the menubar features.
        if(com == "inspect_m"):
            webbrowser.open("https://edgeimpulse.com/")
        if(com == "inspect_e"):
            webbrowser.open("https://docs.edgeimpulse.com/docs/edge-impulse-studio/organizations/data-campaigns")
        if(com == "pre_lesson"):
            subprocess.Popen(["xdg-open", "./audio_lesson"])
        if(com == "o_tutorial"):
            webbrowser.open("https://www.hackster.io/kutluhan-aktar")
        if(com == "h_chatgpt"):
            webbrowser.open("https://platform.openai.com/docs/api-reference/introduction")
            
    def show_interface(self):
        # Show the designed user interface (GUI) on the screen.
        self.app.display()

main.py

Python
# AI-driven Interactive Lab Assistant w/ OpenCV & ChatGPT
#
# NVIDIA Jetson Nano
#
# By Kutluhan Aktar
#
# In remote learning, provide insightful guidance on lab equipment as auto-generated lessons
# via object detection and artificial intelligence. 
# 
#
# For more information:
# https://www.hackster.io/kutluhan-aktar


from _class import lab_assistant_op
from threading import Thread


# Define the lab_assistant object.
lab_assistant = lab_assistant_op("model/ai-driven-interactive-lab-assistant-linux-aarch64.eim")

# Define and initialize threads.
Thread(target=lab_assistant.camera_feed).start()

# Activate the user interface (GUI).
lab_assistant.show_interface()

Credits

Kutluhan Aktar

Kutluhan Aktar

82 projects • 310 followers
AI & Full-Stack Developer | @EdgeImpulse | @Particle | Maker | Independent Researcher

Comments

Related channels and tags
  • Anycubic
  • Autodesk
  • Displays
  • Displays
  • Edge Impulse
  • Elecrow
  • Home Automation
  • Home Automation
  • Internet of Things
  • Internet of Things
  • Machine Learning & AI
  • Machine Learning & AI
  • Microsoft
  • NVIDIA
  • OpenCV
  • SparkFun