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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pikatchu_vs_raichu_vs_snorlax.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,59 @@
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Load the trained model
+model_path = "pikatchu_vs_raichu_vs_snorlax.keras"
+model = tf.keras.models.load_model(model_path)
+
+# Define the core prediction function
+def predict_pokemon(image):
+    # Preprocess image
+    image = image.resize((150, 150))  # 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
+    
+    # Predict
+    prediction = model.predict(image)
+    
+    # Apply softmax to get probabilities for each class
+    probabilities = tf.nn.softmax(prediction, axis=1)
+    
+    # Map probabilities to Pokemon classes
+    class_names = ['Pikachu', 'Raichu', 'Snorlax']
+    probabilities_dict = {pokemon_class: round(float(probability), 2) for pokemon_class, probability in zip(class_names, probabilities.numpy()[0])}
+    
+    return probabilities_dict
+
+# Streamlit interface
+st.title("Pokemon Classification Tool")
+st.write("A basic MLP classifier for image recognition utilizing a pre-trained model.")
+
+# Upload image
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png"])
+
+if uploaded_image is not None:
+    image = Image.open(uploaded_image)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+    
+    predictions = predict_pokemon(image)
+    
+    # Display predictions as a DataFrame
+    st.write("### Prediction Probabilities")
+    df = pd.DataFrame(predictions.items(), columns=["Pokemon", "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.set_xlim(0, 1)
+    ax.set_xlabel('Probability')
+    ax.set_title('Prediction Probabilities')
+    st.pyplot(fig)

pikatchu_vs_raichu_vs_snorlax.keras

@@ -0,0 +1,3 @@
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+oid sha256:276fca4de30a03b9e49e29f40526e93c37f8dc3b80de4df72568c2e43ca0cd05
+size 250560272