Upload 4 files

InceptionResNetV2Skripsi.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2ce9b335d23bb9811adffe070040def6a0b8feecff69e345c0d1c16364804bfb
+size 56233680

another.py

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+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import cv2
+
+# st.markdown(
+#      """
+#      <style>
+#      .stApp {
+#         background-image:url( "https://cdn.discordapp.com/attachments/1086260179139579955/1099734972971102298/img.png");
+#         background-size: cover;
+#      }
+#      </style>
+#      """,
+#      unsafe_allow_html=True
+# )
+
+labels = ['Actinic Keratoses', 'Basal Cell Carcinoma', 'Benign Keratosis-like Lesions', 'Dermatofibroma', 'Melanoma', 'Melanocytic Nevi', 'Vascular Lesions']
+
+model = tf.keras.models.load_model('front_model_resnet.h5')
+classify_model=tf.lite.Interpreter(model_path="InceptionResNetV2Skripsi.tflite")
+classify_model.allocate_tensors()
+
+input_details = classify_model.get_input_details()
+output_details = classify_model.get_output_details()
+
+def detect_skin(image):
+    # Convert the image to YCrCb color space
+    ycrcb = cv2.cvtColor(image, cv2.COLOR_BGR2YCrCb)
+    
+    # Apply skin color detection algorithm
+    lower_skin = np.array([0, 133, 77], dtype=np.uint8)
+    upper_skin = np.array([255, 173, 127], dtype=np.uint8)
+    mask = cv2.inRange(ycrcb, lower_skin, upper_skin)
+    
+    # Apply morphological transformations to remove noise
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
+    mask = cv2.erode(mask, kernel, iterations=2)
+    mask = cv2.dilate(mask, kernel, iterations=2)
+    
+    # Count the number of skin pixels
+    num_skin_pixels = cv2.countNonZero(mask)
+    
+    # Calculate the ratio of skin pixels to total pixels
+    ratio = num_skin_pixels / (image.shape[0] * image.shape[1])
+    
+    return ratio
+
+def resize_image(image):
+    # Resize the image to 150x150 pixels
+    resized_image = tf.image.resize(image, [150, 150])
+    return resized_image.numpy()
+
+def classify_image1(image):
+    # Pre-process the input image
+    resized_image = resize_image(image)
+    input_data = np.expand_dims(resized_image, axis=0).astype(np.float32)
+    classify_model.set_tensor(input_details[0]['index'], input_data)
+
+    # Run inference
+    with st.spinner('Classifying...'):
+        classify_model.invoke()
+
+    # Get the output probabilities
+    output_data = classify_model.get_tensor(output_details[0]['index'])
+    return output_data[0]
+def classify_image(img, model):
+    image=img
+    img = img.resize((224, 224))  # Resize the image to match the model input size
+    img_array = np.array(img)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    prediction = model.predict(img_array)
+    if prediction[0][0] > 0.5:
+        st.write("The image is classified as class Cancer")
+        # image = np.array(Image.open(image))
+        # st.image(image, width=150)
+
+        # Run inference on the input image
+        probs = classify_image1(image)
+
+        # # Display the top 3 predictions
+        top_3_indices = np.argsort(probs)[::-1][:3]
+        st.write("Top 3 predictions:")
+        for i in range(3):
+            st.write("%d. %s (%.2f%%)" % (i + 1, labels[top_3_indices[i]], probs[top_3_indices[i]] * 100))
+        ind=probs.argmax()
+        st.write("The Most possible label Will be:",labels[ind])
+    else:
+        st.write("The image is classified as class non cancer")
+
+
+# Load the pre-trained model
+model = tf.keras.models.load_model('front_model_resnet.h5')
+classify_model=tf.lite.Interpreter(model_path="InceptionResNetV2Skripsi.tflite")
+classify_model.allocate_tensors()
+
+
+# Define the Streamlit app
+st.title("Skin Cancer Detection")
+st.sidebar.title('Input Image')
+st.sidebar.markdown('Upload an image of a skin lesion to make a prediction.')
+uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png","HEIC"])
+if uploaded_file is not None:
+    image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
+    image = cv2.resize(image, (500, 500))
+    # image = cv2.resize(image, (224, 224))
+        
+    # Detect skin in the image
+    ratio = detect_skin(image)
+        
+    # Display the result
+    # st.image(image, caption="Uploaded Image", use_column_width=True)
+    st.write(f"Ratio of skin pixels to total pixels: {ratio:.2f}")
+    if ratio > 0.4:
+        st.write("The image contains skin.")
+        image = Image.open(uploaded_file)
+        st.image(image, width=300)
+        st.write("")
+        st.write("Classifying...")
+        label = classify_image(image, model)
+    else:
+        st.write("The image does not contain skin.")
+
+    

front_model_resnet.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:60b5551c9ffeb968697abcd30c115b78de96747c34ded16bb86d82848b001ce7
+size 711461336

requirements.txt

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+streamlit
+numpy 
+panda 
+tensorflow
+keras
+matplotlib
+