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InceptionResNetV2Skripsi.tflite

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

app1.py

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+import streamlit as st
+import numpy as np
+import tensorflow as tf
+
+# Load TensorFlow Lite model
+interpreter = tf.lite.Interpreter(model_path="InceptionResNetV2Skripsi.tflite")
+interpreter.allocate_tensors()
+
+# Get input and output tensors
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Define a function to resize the input image
+def resize_image(image):
+    # Resize the image to 150x150 pixels
+    resized_image = tf.image.resize(image, [150, 150])
+    return resized_image.numpy()
+
+# Define a function to run inference on the TensorFlow Lite model
+def classify_image(image):
+    # Pre-process the input image
+    resized_image = resize_image(image)
+    input_data = np.expand_dims(resized_image, axis=0).astype(np.float32)
+    interpreter.set_tensor(input_details[0]['index'], input_data)
+
+    # Run inference
+    with st.spinner('Classifying...'):
+        interpreter.invoke()
+
+    # Get the output probabilities
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    return output_data[0]
+
+# Define the labels for the 7 classes
+labels = ['akiec', 'bcc', 'bkl', 'df', 'mel', 'nv', 'vasc']
+
+from PIL import Image
+# Define the main Streamlit app
+def main():
+    st.title("Skin Cancer Classification")
+
+    st.write("Please note that this model still has room for academic revision as it can only classify the following 7 classes")
+    st.text("'akiec' - squamous cell carcinoma (actinic keratoses dan intraepithelial carcinoma),")
+    st.text("'bcc' - basal cell carcinoma, 'bkl' - benign keratosis (serborrheic keratosis),")
+    st.text("'df' - dermatofibroma, 'nv' - melanocytic nevi, 'mel' - melanoma,")
+    st.text("'vasc' - vascular skin lesions (Cherry Angiomas, Angiokeratomas, Pyogenic Granulomas.")
+    st.write("Due to imperfection of the model and a room of improvement for the future, if the probabilities shown are less than 70%, the skin is either healthy or the input image is unclear. This means that the model can be the first diagnostic of your skin illness. As precautions for your skin illness, it is better to do consultation with dermatologist. ")
+
+    # Get the input image from the user
+    image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+    # Show the input image
+    if image is not None:
+        image = np.array(Image.open(image))
+        st.image(image, width=150)
+
+        # Run inference on the input image
+        probs = classify_image(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))
+
+if __name__ == '__main__':
+    main()