ocr.py

import asyncio
import cv2
import os
import time
import easyocr
import requests
import re
from websockets.server import serve

from secret import OPENAI_API_KEY

# Set your OpenAI API key here
#OPENAI_API_KEY = TEMPLATE

# Initialize EasyOCR reader
reader = easyocr.Reader(['en'])

# Function to preprocess text
def preprocess_text(text):
    text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
    text = re.sub(r'\s+', ' ', text)
    return text.strip()

# Function to analyze text sentiment
def analyzeTextSentiment(prompt):
    url = "https://api.openai.com/v1/chat/completions"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {OPENAI_API_KEY}"
    }

    data = {
        "model": "gpt-3.5-turbo-0613",
        "max_tokens": 200,
        "temperature": 0.7,
        "top_p": 1,
        "n": 1,
        "messages": [
            {"role": "system", "content": "You are a system that understands the data gathered from a book using OCR, analyzes the text and then returns the predominant feeling in a single word. You can only pick from the list: happy, sad, angry, excited, confused or neutral. Keep in mind that you are reading children books so the feelings are not too complex, you are allowed to predict the feeling that the user would feel in certain situations. Just make sure to pick from the given list."},
            {"role": "user", "content": "Please analyze the text and determine the predominant feeling. Answer in a single word from the list: happy, sad, angry or neutral." + prompt},
            {"role": "assistant", "content": "You can only answer with a single word, from the list: happy, sad, angry or neutral. write the answer in lower case."},
        ]
    }

    response = requests.post(url, json=data, headers=headers)

    if response.status_code == 200:
        response_json = response.json()
        sentiment = response_json["choices"][0]["message"]["content"]
        sentiment = preprocess_text(sentiment)
        return sentiment
    else:
        print("Error:", response.status_code)
        print("Response Content:", response.content)
        return "Error analyzing text"

async def send_result(websocket, path):
    print("Client connected")

    # Capture frame-by-frame
    cap = cv2.VideoCapture(0)  # adjust, 0 = computer webcam, 1 = external webcam
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1080)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)


    # Check if the webcam is opened correctly
    if not cap.isOpened():
        print("Error: Could not open webcam")
        exit()

    try:
        while True:
            # Capture frame-by-frame
            ret, frame = cap.read()

            if not ret:
                print("Error: Could not read frame")
                break

            # Save the image with a unique filename (timestamp)
            timestamp = time.strftime("%Y%m%d-%H%M%S")
            filename = f"captured_image_{timestamp}.jpg"
            cv2.imwrite(filename, frame)

            # Read text from the captured image
            result = reader.readtext(filename)

            # Print the detected text
            detected_text = ""
            if result:
                print("Detected Text:")
                for detection in result:
                    print(detection[1])
                    detected_text += detection[1] + " "

                print("Volledige OCR tekst: ", detected_text)

                # Generate sentiment based on detected text
                sentiment = analyzeTextSentiment(detected_text)
                print(sentiment)

                # Send the sentiment over the WebSocket connection
                await websocket.send(sentiment)

            # Wait for 10 seconds
            await asyncio.sleep(10) #await

    finally:
        # Release the capture
        cap.release()
        cv2.destroyAllWindows()

async def main():
    print("Starting server...")
    async with serve(send_result, "localhost", 8765):
        print("Server started on ws://localhost:8765")
        await asyncio.Future()  # run forever

asyncio.run(main())