Add application file

app.py

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+import streamlit as st
+import numpy as np
+from PIL import Image
+import joblib
+from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
+import cv2
+
+# Load the trained KNN model
+model_file_path = 'skin_cancer_model.pkl'
+model = joblib.load(model_file_path)
+
+# Function to preprocess the image
+def preprocess_image(image):
+    # Resize the image to a fixed size (e.g., 128x128)
+    size = (224, 224)
+    image = image.resize(size)
+    # Convert the image to a numpy array
+    image_array = np.array(image)
+    # Flatten the image array
+    image_flattened = image_array.flatten()
+    return image_flattened
+
+# Define a video transformer class to process video frames
+class VideoTransformer(VideoTransformerBase):
+    def transform(self, frame):
+        img = frame.to_ndarray(format="bgr24")
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        
+        # Preprocess image for the model (resize to match model input size)
+        img_resized = cv2.resize(img, (224, 224))  # Adjust size as per your model input
+        img_resized = img_resized.astype("float32") / 255.0
+        img_resized = np.expand_dims(img_resized, axis=0)  # Add batch dimension
+
+        # Make prediction
+        prediction = model.predict(img_resized)
+        predicted_class = np.argmax(prediction, axis=1)[0]
+
+        # Display predicted class
+        #cv2.putText(img, f"Predicted Class: {predicted_class}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        st.write(f"Skin Cancer Prediction: {predicted_class}")
+
+        return img
+
+def main():
+    st.title("Skin Diagnosis")
+
+    st.write("Please note that this app is not 100% accurate. If your result happens to be malignant, please contact a medical professional for further instructions.")
+
+    # Use the webrtc_streamer to capture video from the camera
+    webrtc_streamer(key="example", video_processor_factory=VideoTransformer)
+
+    st.write("**Upload an image to detect if it's benign or malignant:**")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+
+        # Preprocess the image
+        processed_image = preprocess_image(image)
+
+        # Make prediction
+        prediction = model.predict([processed_image])[0]
+        class_names = {0: 'Benign', 1: 'Malignant'}
+        result = class_names[prediction]
+
+        st.write(f"Skin Cancer Prediction: {result}")
+    st.write("**Please click the button if you have had a large amount of sun exposure:**")
+    result = st.button("Yes")
+    if result:
+        st.write("Please note that high levels of sun exposure can lead to a higher risk of skin cancer.")
+
+if __name__ == '__main__':
+    main()