Routes

routes.py

+from flask import render_template, request
+from app import app
+import numpy as np  # linear algebra
+import pandas as pd  # data processing, CSV file I/O (e.g. pd.read_csv)
+import os
+import cv2
+from pathlib import Path
+import pickle
+import tensorflow as tf
+import sklearn
+
+
+@app.route('/skincancer')
+def skincancer():
+    return render_template('skincancer.html')
+
+
+def predict2(img):
+    model = tf.keras.models.load_model('modelNew.h5')
+
+    pic = []
+    img = cv2.resize(img, (100, 100))
+    if img.shape[2] == 1:
+        img = np.dstack([img, img, img])
+    img = np.array(img)
+    img = img/255
+    # label = to_categorical(0, num_classes=2)
+    pic.append(img)
+    # pic_labels.append(pneu)
+    pic1 = np.array(pic)
+    a = model.predict(pic1)
+    print(a.argmax())
+    predicts = a.argmax()
+    predicts
+
+    if predicts == 0:
+        return "Melanocytic nevi"
+    elif predicts == 1:
+        return "Melanoma"
+    elif predicts == 2:
+        return "Benign keratosis-like lesions"
+    elif predicts == 3:
+        return "Basal cell carcinoma"
+    elif predicts == 4:
+        return "Actinic keratoses"
+    elif predicts == 5:
+        return "Vascular lesions"
+    else:
+        return "Dermatofibroma"
+
+
+@app.route('/process_image_skin', methods=['POST'])
+def process_image_skin():
+    if request.method == 'POST':
+        file = request.files['file']
+        img_bytes = file.read()  # Read image data as bytes
+        # Convert bytes to OpenCV image
+        img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), -1)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
+        prediction = predict2(img)  # Make prediction using img
+        return render_template('results.html', result=prediction)
+
+    return 'File uploaded successfully!'