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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
+pokemon-model_transferlearning1.keras filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,13 @@
 ---
-title: Skin
-emoji: 👁
-colorFrom: blue
-colorTo: green
+title: Pokemon
+emoji: 🏃
+colorFrom: pink
+colorTo: red
 sdk: streamlit
-sdk_version: 1.35.0
+sdk_version: 1.34.0
 app_file: app.py
 pinned: false
+license: other
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+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 = "pokemon-model_transferlearning1.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 = ['Chansey', 'Growlithe', 'Lapras']
+    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 Classifier")
+st.write("Welches Pokemon hast du ausgewählt?")
+
+# 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)
+
+# 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)

gitattributes.txt

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+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+pokemon-model_transferlearning.keras filter=lfs diff=lfs merge=lfs -text
+pokemon-model_transferlearning1.keras filter=lfs diff=lfs merge=lfs -text
+pokemon/00000001.png filter=lfs diff=lfs merge=lfs -text

pokemon-model_transferlearning1.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8f63f7dc8b294af5a9028375143425ee082c5239aaa700524570641c1c3e97d6
+size 250560147

requirements.txt

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+tensorflow==2.16.1
+protobuf==3.20.3
+numpy
+streamlit
+pillow