Added app file

app.py

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+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import io
+
+# Set seed for reproducibility
+np.random.seed(42)
+tf.random.set_seed(42)
+
+# Define the correct class labels mapping
+class_labels = ['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc', 'mel']
+
+# Load the trained model
+@st.cache_resource
+def load_model():
+    return tf.keras.models.load_model('final_model.h5')
+
+model = load_model()
+
+# Load mean and std from training (you'll need to provide these)
+@st.cache_resource
+def load_mean_std():
+    mean = np.load('mean.npy')
+    std = np.load('std.npy')
+    return mean, std
+
+mean, std = load_mean_std()
+
+
+
+# Define the image preprocessing function
+def preprocess_image(image):
+    image = image.resize((28, 28))
+    image = np.asarray(image)
+    image = (image - mean) / std
+    image = np.expand_dims(image, axis=0)
+    return image
+
+# Streamlit app
+st.title('Skin Lesion Predictor')
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(io.BytesIO(uploaded_file.read())).convert('RGB')
+    st.image(image, caption='Uploaded Image', use_contanier_width=True)
+    
+    # Preprocess the image
+    processed_image = preprocess_image(image)
+    
+    # Make prediction
+    predictions = model.predict(processed_image)
+    
+    # Get predicted class index and name
+    predicted_class_idx = np.argmax(predictions, axis=1)[0]
+    predicted_class = class_labels[predicted_class_idx]
+    confidence = predictions[0][predicted_class_idx]
+    
+    # Display results
+    st.write(f"Predicted Class: {predicted_class}")
+    st.write(f"Confidence: {confidence:.2f}")
+    
+    # Display bar chart of all predictions
+    st.bar_chart(dict(zip(class_labels, predictions[0])))
+
+st.write("Note: This is a demo application and should not be used for medical diagnosis. Always consult with a healthcare professional.")