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Runtime error
Runtime error
Upload 8 files
Browse files- app/app.py +86 -0
- app/static/images/background.png +0 -0
- app/static/images/brandName.png +0 -0
- app/static/images/chat.png +0 -0
- app/static/images/detectPhone.png +0 -0
- app/static/images/errorScreenshot.png +0 -0
- app/static/myStyles.css +97 -0
- app/templates/index.html +140 -0
app/app.py
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from flask import Flask, request, jsonify, render_template
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from flask_cors import CORS
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import cv2
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import easyocr
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import numpy as np
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import tensorflow as tf
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app = Flask(__name__)
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CORS(app)
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# Load your pre-trained model and set up the test set for phone recognition
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test_set = tf.keras.utils.image_dataset_from_directory(
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"/app/testing_data",
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labels="inferred",
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label_mode="categorical",
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class_names=None,
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color_mode="rgb",
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batch_size=32,
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image_size=(64, 64),
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shuffle=True,
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seed=None,
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validation_split=None,
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subset=None,
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interpolation="bilinear",
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follow_links=False,
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crop_to_aspect_ratio=False
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)
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# Load the CNN model for phone recognition
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cnn = tf.keras.models.load_model("/app/trained_model (1).h5")
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# Instantiate text reader for OCR
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picture_read = easyocr.Reader(["en", "ar"], gpu=False)
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def get_class_names():
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# Load the class names from the test_set
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return test_set.class_names
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class_names = get_class_names()
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# Route for rendering the index.html template
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@app.route("/")
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def index():
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# Pass the class names to the HTML template
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return render_template("index.html", class_names=class_names)
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# Route for phone recognition and processing image
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@app.route("/process_image", methods=["POST"])
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def process_image():
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# Get the uploaded file from the request
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uploaded_file = request.files["file"]
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# Save the file to a temporary location
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temp_file_path = "/app/temp_image.png"
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uploaded_file.save(temp_file_path)
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# Read and preprocess the image for phone model recognition
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img = cv2.imread(temp_file_path)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = cv2.resize(img, (64, 64))
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# Make predictions for phone model
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input_arr = np.array([img])
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predictions = cnn.predict(input_arr)
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# Get the index with the highest probability
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result_index = np.argmax(predictions)
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# Display the predicted class for phone model
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result_phone_model = class_names[result_index]
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# Process image using OCR
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picture_results = picture_read.readtext(temp_file_path)
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results_ocr = []
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conf_threshold = 0.2
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for y in picture_results:
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if y[2] > conf_threshold:
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text = y[1]
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results_ocr.append({"text": text})
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# Return JSON response with the results
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return jsonify({"result_phone_model": result_phone_model, "results_ocr": results_ocr})
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if __name__ == "__main__":
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app.run(host="0.0.0.0", port=8000, debug=True)
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app/static/images/background.png
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app/static/images/brandName.png
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app/static/images/chat.png
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app/static/images/detectPhone.png
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app/static/images/errorScreenshot.png
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app/static/myStyles.css
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body {
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background-image: url(images/background.png);
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background-size: cover;
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background-repeat: no-repeat;
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display: flex;
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flex-direction: column;
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align-items: center;
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justify-content: center;
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height: 100vh;
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margin: 0;
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}
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.brand-container {
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display: flex;
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flex-direction: column;
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align-items: center;
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margin-top: 13%;
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text-align: center;
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}
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.brand-container img {
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display: block;
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width: 130%;
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max-width: 1500px;
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}
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span {
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color: #f3f3f3;
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font-family: Arial, Helvetica, sans-serif;
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font-size: 30px;
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margin-top: 5%;
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}
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.buttons-container {
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display: flex;
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align-items: center;
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justify-content: center;
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padding-right: 0;
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margin-top: 10%;
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}
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label {
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cursor: pointer;
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margin: -3px;
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}
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input[type="file"] {
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display: none;
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margin: 0;
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padding: 0;
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}
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button {
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display: none;
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}
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label img {
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transition: transform 0.2s ease-in-out;
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}
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label:hover img {
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transform: scale(1.1);
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}
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/*loading screen*/
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#loading-screen {
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position: fixed;
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top: 0;
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left: 0;
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width: 100%;
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height: 100%;
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background-image: url(images/background.png);
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background-size: cover;
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background-repeat: no-repeat;
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display: flex;
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justify-content: center;
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align-items: center;
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z-index: 9999;
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}
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.loading-spinner {
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border: 8px solid lightgray;
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border-top: 8px solid silver;
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border-radius: 50%;
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width: 50px;
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height: 50px;
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animation: spin 1s linear infinite;
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}
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@keyframes spin {
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0% {
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transform: rotate(0deg);
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}
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100% {
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transform: rotate(360deg);
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}
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}
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app/templates/index.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Mobile Diagnostics</title>
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<link rel="stylesheet" href="/static/myStyles.css" />
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<script src="/static/script.js"></script>
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<script src="https://cdn.botpress.cloud/webchat/v1/inject.js"></script>
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<script src="https://mediafiles.botpress.cloud/9f157b03-6640-43c0-b364-fdf445bdcbcb/webchat/config.js" defer></script>
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</head>
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<body>
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<div id="loading-screen">
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<div class="loading-spinner"></div>
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</div>
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<div class="brand-container">
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<img src="/static/images/brandName.png" alt="Mobile Diagnostics" width="1500px" />
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<span>Your troubleshooting buddy on the go.</span>
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</div>
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<div class="buttons-container" id="buttons">
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<label for="upload">
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<img src="/static/images/detectPhone.png" alt="Upload Image" />
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<input type="file" id="upload" accept=".png, .jpg, .jpeg" />
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</label>
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<label for="screenshot">
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<img src="/static/images/errorScreenshot.png" alt="Screenshot Image" />
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<input type="file" id="screenshot" accept=".png, .jpg, .jpeg" />
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</label>
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<label for="chat">
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<img src="/static/images/chat.png" alt="Chat with Us" />
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<button id="chat">Chat</button>
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</label>
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<div id="result"></div>
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</div>
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<script>
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document.addEventListener("DOMContentLoaded", function () {
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setTimeout(function () {
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document.getElementById("loading-screen").style.display = "none";
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}, 3000);
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});
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const upload = document.getElementById("upload");
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const screenshot = document.getElementById("screenshot");
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const chat = document.getElementById("chat");
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function showBot() {
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window.botpressWebChat.sendEvent({ type: "show" });
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}
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function hideButtons() {
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document.getElementById("buttons").style.display = "none";
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}
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upload.addEventListener("change", function () {
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var fileInput = document.getElementById("upload");
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if (fileInput.files.length > 0) {
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const file = fileInput.files[0];
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const formData = new FormData();
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formData.append("file", file);
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fetch("/phone-recognition", {
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method: "POST",
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body: formData,
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})
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.then((response) => response.json())
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.then((result) => {
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const result_phone_model = result.result_phone_model;
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console.log(result_phone_model);
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window.botpressWebChat.sendPayload({
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type: "text",
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payload: {
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result_phone_model,
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pressedButton: "upload",
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},
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});
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hideButtons();
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showBot();
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})
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.catch((error) => {
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console.error("Error during phone recognition:", error);
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});
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}
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});
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screenshot.addEventListener("change", function () {
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var screenshotInput = document.getElementById("screenshot");
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if (screenshotInput.files.length > 0) {
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const file = screenshotInput.files[0];
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const formData = new FormData();
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formData.append("file", file);
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fetch("/phone-recognition", {
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method: "POST",
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body: formData,
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})
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.then((response) => response.json())
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.then((result) => {
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const results_ocr = result.results_ocr;
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console.log(results_ocr);
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window.botpressWebChat.sendPayload({
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type: "text",
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payload: {
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results_ocr,
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pressedButton: "screenshot",
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},
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});
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hideButtons();
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showBot();
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})
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.catch((error) => {
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console.error("Error during image processing:", error);
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});
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}
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});
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// Event listener for chat button
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chat.addEventListener("click", function () {
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window.botpressWebChat.sendPayload({
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type: "text",
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payload: {
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pressedButton: "chat",
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},
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});
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hideButtons();
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showBot();
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});
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</script>
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</body>
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</html>
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