Update app.py

Update to Skin

app.py

@@ -6,13 +6,13 @@ import pandas as pd
 import matplotlib.pyplot as plt
 
 # Load the trained model
-model_path = "pokemon-model_transferlearning1.keras"
+model_path = "skin-model_transferlearning.keras"
 model = tf.keras.models.load_model(model_path)
 
 # Define the core prediction function
-def predict_pokemon(image):
+def predict_skin(image):
     # Preprocess image
-    image = image.resize((150, 150))  # Resize the image to 150x150
+    image = image.resize((450, 450))  # Resize the image to 150x150
     image = image.convert('RGB')  # Ensure image has 3 channels
     image = np.array(image)
     image = np.expand_dims(image, axis=0)  # Add batch dimension
@@ -23,15 +23,15 @@ def predict_pokemon(image):
     # Apply softmax to get probabilities for each class
     probabilities = tf.nn.softmax(prediction, axis=1)
     
-    # Map probabilities to Pokemon classes
-    class_names = ['Chansey', 'Growlithe', 'Lapras']
-    probabilities_dict = {pokemon_class: round(float(probability), 2) for pokemon_class, probability in zip(class_names, probabilities.numpy()[0])}
+    # Map probabilities to Skin condition classes
+    class_names = ['AKIEC', 'BCC', 'BKL', 'DF', 'MEL', 'NV', 'VASC']
+    probabilities_dict = {skin_class: round(float(probability), 2) for skin_class, probability in zip(class_names, probabilities.numpy()[0])}
     
     return probabilities_dict
 
 # Streamlit interface
-st.title("Pokemon Classifier")
-st.write("Welches Pokemon hast du ausgewählt?")
+st.title("Skin disease classifier")
+st.write("Choose a picture:")
 
 # Upload image
 uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png"])
@@ -42,24 +42,18 @@ if uploaded_image is not None:
     st.write("")
     st.write("Classifying...")
     
-    predictions = predict_pokemon(image)
+    predictions = predict_skin(image)
     
     # Display predictions as a DataFrame
     st.write("### Prediction Probabilities")
-    df = pd.DataFrame(predictions.items(), columns=["Pokemon", "Probability"])
+    df = pd.DataFrame(predictions.items(), columns=["Condition", "Probability"])
     st.dataframe(df)
     
     # Display predictions as a bar chart
     st.write("### Prediction Chart")
     fig, ax = plt.subplots()
-    ax.barh(df["Pokemon"], df["Probability"], color='skyblue')
+    ax.barh(df["Condition"], df["Probability"], color='skyblue')
     ax.set_xlim(0, 1)
     ax.set_xlabel('Probability')
     ax.set_title('Prediction Probabilities')
-    st.pyplot(fig)
-
-# Example images
-st.sidebar.title("Examples")
-example_images = ["pokemon/00000000.png","pokemon/00000001.png","pokemon/00000002.png"]
-for example_image in example_images:
-    st.sidebar.image(example_image, use_column_width=True)
+    st.pyplot(fig)