base version

main.py

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+import os
+
+os.environ["KERAS_BACKEND"] = "jax"
+
+import streamlit as st
+from vision_models import vision_page
+
+
+
+def main():
+    # Set up the main layout and title
+    st.set_page_config(page_title="ModelLens", layout="centered")
+    st.title("ModelLens")
+
+    # Sidebar for navigation
+    st.sidebar.title("Navigation")
+    options = ["Vision", "NLP", "About"]
+    choice = st.sidebar.radio("Go to", options)
+
+    # Route to the selected page
+    if choice == "Vision":
+        vision_page()
+    elif choice == "NLP":
+        nlp_page()
+    elif choice == "About":
+        about_page()
+
+
+def nlp_page():
+    st.header("Natural Language Processing")
+    st.write("This section is for exploring NLP models.")
+    # Add your NLP model visualization or interaction code here
+
+
+def about_page():
+    st.header("About Page")
+    st.write(
+        "This app was created to demonstrate a basic Streamlit application layout."
+    )
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+keras-hub==0.18.1
+streamlit==1.41.1
+keras==3.8.0
+ruff==0.9.3
+jax==0.5.0
+tensorflow==2.18.0
+tensorflow_text==2.18.1

vision_models.py

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+import streamlit as st
+import keras_hub
+from PIL import Image
+import numpy as np
+
+classification_models = {
+    "ResNet18": "resnet_18_imagenet",
+    "ResNet50": "resnet_50_imagenet"
+}
+
+
+def load_preprocessor(model_name):
+    return keras_hub.models.ImageClassifierPreprocessor.from_preset(model_name)
+
+def load_model(model_name):
+    """Load a pre-trained model from KerasHub."""
+    return keras_hub.models.ImageClassifier.from_preset(model_name)
+
+def upload_image():
+    """Common function for uploading an image."""
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    if uploaded_file:
+        image = Image.open(uploaded_file)
+        return np.expand_dims(np.array(image).astype("float32"), axis=0)
+    return None
+
+def vision_page():
+    st.header("Vision Models")
+    st.write("Explore Vision Models including Image Classification, Object Detection, and Segmentation.")
+
+    # Tabs for different vision tasks
+    tab1, tab2, tab3 = st.tabs(["Image Classification", "Object Detection", "Segmentation"])
+
+    with tab1:
+        st.subheader("Image Classification")
+        model_name = st.selectbox("Choose a pre-trained model:", list(classification_models.keys()))
+        preprocessor = load_preprocessor(classification_models[model_name])
+        model = load_model(classification_models[model_name])
+
+        image = upload_image()
+        if image is not None:
+            preprocessed_image = preprocessor(image)
+            raw_predictions = model(preprocessed_image)
+            predictions = keras_hub.utils.decode_imagenet_predictions(raw_predictions)
+
+            col1, col2 = st.columns([1, 1])
+            with col1:
+                st.image(image[0].astype("uint8"), caption="Uploaded Image", use_container_width=True)
+            with col2:
+                st.write("##### Top Predictions:")
+                for idx, (class_name, score) in enumerate(predictions[0]):
+                    st.write(f"{idx + 1}: {class_name}")
+
+    with tab2:
+        st.subheader("Object Detection")
+        st.write("Object Detection functionality is under development.")
+
+    with tab3:
+        st.subheader("Segmentation")
+        st.write("Segmentation functionality is under development.")