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import os |
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from flask import Flask, render_template, request |
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from flask_cors import cross_origin, CORS |
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import tensorflow.lite as lite |
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from tensorflow.keras.applications.mobilenet_v2 import preprocess_input |
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from utils.make_upload_dir import make_upload_dir |
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from tensorflow.keras.utils import load_img, save_img, img_to_array |
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from numpy import array, round |
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from utils.delete_file import delete_file |
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from requests import get |
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from functools import lru_cache |
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app = Flask(__name__, template_folder='templates', static_folder='static') |
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CORS(app) |
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CLASS_INDICES = {'Bean': 0, |
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'Bitter_Gourd': 1, |
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'Bottle_Gourd': 2, |
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'Brinjal': 3, |
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'Broccoli': 4, |
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'Cabbage': 5, |
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'Capsicum': 6, |
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'Carrot': 7, |
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'Cauliflower': 8, |
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'Cucumber': 9, |
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'Papaya': 10, |
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'Potato': 11, |
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'Pumpkin': 12, |
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'Radish': 13, |
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'Tomato': 14} |
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@lru_cache(maxsize=1) |
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def load_model(model_path): |
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interpreter = lite.Interpreter(model_path=model_path) |
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print('loaded model') |
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return interpreter |
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interpreter = load_model(model_path=os.path.join("models", "vegetable_classification_model_mnet.tflite")) |
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def predict(test_image): |
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interpreter.allocate_tensors() |
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input_details = interpreter.get_input_details()[0] |
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output_details = interpreter.get_output_details()[0] |
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test_image = test_image.astype(input_details["dtype"]) |
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interpreter.set_tensor(input_details["index"], test_image) |
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interpreter.invoke() |
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output = interpreter.get_tensor(output_details["index"])[0] |
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prediction = [*CLASS_INDICES.keys()][output.argmax()] |
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probability = round(output.max() * 100, 2) |
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result = f'{prediction} ({probability}%)' |
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return result |
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@app.route("/") |
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@cross_origin() |
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def index(): |
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""" |
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displays the index.html page |
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""" |
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display_image = os.path.join("static", "white.png") |
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delete_file(os.path.join('.', 'static', 'uploads')) |
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delete_file(os.path.join('.', 'static', 'output', 'display.jpg')) |
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return render_template("index.html", display_image=display_image) |
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@app.route("/", methods=["POST"]) |
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@cross_origin() |
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def file_prediction(): |
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upload_file_path = "" |
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result = '' |
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error = "" |
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try: |
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upload_file_path = os.path.join('.', 'static', 'uploads') |
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make_upload_dir(upload_file_path) |
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upload_filename = "input.jpg" |
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upload_file_path = os.path.join(upload_file_path, upload_filename) |
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file_url = request.form['fileinput'] |
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headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'} |
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r = get(file_url, headers=headers) |
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with open(upload_file_path, "wb") as file: |
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file.write(r.content) |
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test_image = load_img(upload_file_path, color_mode="rgb", target_size=(224, 224)) |
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test_image = img_to_array(test_image) |
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test_image = preprocess_input(test_image) |
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test_image = array([test_image]) |
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display_image = os.path.join('.', 'static', 'output', 'display.jpg') |
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save_img(path=display_image, x=test_image[0]) |
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result = predict(test_image) |
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delete_file(upload_file_path) |
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except Exception as e: |
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result = '' |
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error = e |
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display_image = os.path.join("static", "white.png") |
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delete_file(upload_file_path) |
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return render_template("index.html", result=result, error=error, display_image=display_image) |
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if __name__ == "__main__": |
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app.run(host="0.0.0.0", port=5002) |
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