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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Model_2021_CNN_Xception-V09.hdf5 filter=lfs diff=lfs merge=lfs -text

Model_2021_CNN_Xception-V09.hdf5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:95d36d811b2876ca2608cbaae73edd1a38e8f8b18e9c15a2745c1465f3f4e46e
+size 95097856

Procfile

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+web: python app.py
+
+

app.py

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+import os
+from flask import Flask, flash, request, redirect, url_for, render_template
+from werkzeug.utils import secure_filename
+import math
+
+#  export FLASK_APP=app
+#  flask run
+
+arquivo_modelo = 'Model_2021_CNN_Xception-V09.hdf5' #'Model_2021_CNN_VGG19-V01.hdf5' # 'model_Titan-v02.hdf5' Só CCN
+
+
+UPLOAD_FOLDER = '/tmp'
+ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'}
+
+def escolhe_lesao_aleatoria():
+    import glob
+    from random import seed
+    from random import randint
+
+    arquivos = list(glob.glob("static/tmp/*.*"))
+    arquivos = [ arquivo.split('/')[2] for arquivo in arquivos]
+
+    lesao = randint(0,len(arquivos)-1)
+    print(lesao)
+    return arquivos[lesao]
+
+
+def prever_doencas_de_pele(model, file):
+    import numpy as np
+    from PIL import Image
+    import pandas as pd
+
+    folder = 'static/tmp/'
+
+    dict_idx_doenca = {0: ['Actinic keratoses', 'Queratose Actínica'],
+     1: ['Basal cell carcinoma', 'Carcinoma de Células Basais' ],
+     2: ['Benign keratosis-like lesions ', 'Queratoses Benignas'],
+     3: ['Dermatofibroma', 'Dermatofibroma'], # (Histiocitoma Fibroso Benigno)' ],
+     4: ['Melanocytic nevi', 'Nevo Melanócito (Sinal)'], # (Nevo Pigmentado, Sinal)
+     5: [ 'Melanoma', 'Melanoma'],
+     6: ['Vascular lesions', 'Lesões de Pele Vasculares'],
+     7: ['Acne', 'Acne'],
+     8: ['AlopeciaAreata', 'AlopeciaAreata']}
+
+    indices = []
+    doencas_en = []
+    doencas_pt = []
+    for idx, doenca in (dict_idx_doenca.items()):
+        indices.append(idx)
+        doencas_en.append(doenca[0])
+        doencas_pt.append(doenca[1])
+
+    media_scale_image = 158.4125188825441
+    std_scale_image = 47.42283803971779
+
+
+    x = folder + file
+    #x_pred = np.asarray(Image.open(x).resize((100,75)))
+
+    SIZE = 299 # 224
+    x_pred = np.asarray(Image.open(x).resize((SIZE,SIZE)))
+    x_pred = x_pred.reshape(1, SIZE, SIZE, 3)
+    x_pred = (x_pred - media_scale_image) / std_scale_image
+    #classe = model.predict_classes(x_pred)[0]
+    pred = np.argmax(model.predict(x_pred), axis=-1)
+    probs = model.predict(x_pred)[0]
+
+    probs = np.array(probs) * 100
+    df = pd.DataFrame()
+    df['probs'] = probs
+    print('probs:', probs )
+    df['probs'] = df['probs'].apply(lambda x : int(x))
+    df['doenca_en'] = doencas_en
+    df['doenca_pt'] = doencas_pt
+    df['idx'] = indices
+    df_ordenado = df.sort_values(by=['probs'], ascending=False).reset_index()
+    df_ordenado = df_ordenado[ df_ordenado.probs > 0]
+
+    numero_probilidades_maior_que_zero = len(df_ordenado)
+    if numero_probilidades_maior_que_zero > 3:
+        numero_probilidades_maior_que_zero = 3
+    probs = df_ordenado['probs'][:numero_probilidades_maior_que_zero]
+    doencas = df_ordenado['doenca_pt'][:numero_probilidades_maior_que_zero]
+
+    #probs = df_ordenado['probs'][:3]
+    #doencas = df_ordenado['doenca_pt'][:3]
+
+    #print('diagnóstico:', doenca, ' - prob:', prob)
+    #print(doenca)
+    #print(prob)
+
+    return doencas, probs
+
+
+def allowed_file(filename):
+    return '.' in filename and \
+           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+
+app = Flask(__name__, template_folder='templates')
+app.secret_key = "super secret key"
+
+app.config['UPLOAD_FOLDER'] = 'static/tmp'
+app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024
+
+
+probs = []
+classesprev = []
+model = None
+
+app.add_url_rule('/static', view_func=app.send_static_file)
+@app.route('/', methods=['GET', 'POST'])
+def upload_file():
+    #from app import model
+    global model
+    import numpy as np
+    import tensorflow as tf
+
+    #from keras.models import load_model
+
+
+    if model is None:
+        print('carregando o modelo...')
+        file_model = arquivo_modelo
+
+        from tensorflow import keras
+        # model = keras.models.load_model(file_model)
+        model = tf.keras.models.load_model(file_model,
+                custom_objects={'Functional':tf.keras.models.Model})
+
+    
+
+        #model = tf.keras.models.load_model(file_model)
+        #model = load_model(file_model)
+        print('modelo carregado.')
+
+
+    UPLOAD_FOLDER = '/tmp'
+    ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'}
+
+    if request.method == 'POST':
+        print('request == POST')
+        d = request.form.to_dict(flat=False)
+        print(d)
+
+        if "photo" in d.keys() and  "prever_lesao" in d.keys() and d['photo'][0] != '': # request.form["prever_lesao"]:
+            file  = request.form['photo']
+            doencas, probs = prever_doencas_de_pele(model, file)
+
+            return render_template("index.html",  file='tmp/'+file,  probs=probs, classesprev=doencas)
+
+        else:
+            file = escolhe_lesao_aleatoria()
+            print(file)
+            doencas, probs = prever_doencas_de_pele(model, file)
+
+            return render_template("index.html",  file='tmp/'+file,  probs=probs, classesprev=doencas)
+
+
+    else:
+        print("elsseeeeee")
+        file = escolhe_lesao_aleatoria()
+        doencas, probs = prever_doencas_de_pele(model, file)
+
+        return render_template("index.html",  file='tmp/'+file,  probs=probs, classesprev=doencas) #, upload_file=global_file)
+
+@app.route('/about/')
+def about():
+    return render_template('About.html')
+
+if __name__ == "__main__":
+
+    app.config['SESSION_TYPE'] = 'filesystem'
+    port = int(os.environ.get("PORT", 5000))
+    app.debug = True
+    app.run(host='0.0.0.0', port=port)

requirements-ORIGINAL.txt

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+Flask==1.1.2
+tensorflow==2.0.0
+werkzeug==1.0.1
+pandas==1.0.3
+numpy==1.18.1
+Pillow==7.1.2

requirements.txt

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+Flask==2.0.2
+numpy==1.19.5
+pandas==1.3.4
+Pillow==8.4.0
+tensorflow_cpu==2.5.0
+Werkzeug==2.0.2

run_app.sh

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+flask --app app.py run

runtime.txt

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+python-3.7.10