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src/intel_classifier_v2.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ea564db71ecc2810f6223e3abcb752ca39fb578ec93ca3b1c2247f99684cc45
+size 132216680

src/streamlit_app.py

@@ -3,26 +3,19 @@ import tensorflow as tf
 import numpy as np
 from PIL import Image
 import json
-import os
 
-# Get the directory where streamlit_app.py is located
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+# Load model 
+@st.cache_resource
+def load_model():
+    model = tf.keras.models.load_model("./src/intel_classifier_improved.h5")
+    return model
 
-# Build the paths dynamically
-model_path = os.path.join(BASE_DIR, "intel_classifier_improved.keras")
-json_path = os.path.join(BASE_DIR, "class_indices.json")
-
-st.write(f"Looking for model at: {model_path}")
-
-model = tf.keras.models.load_model(model_path)
-with open(json_path, "r") as f:
-    class_indices = json.load(f)
+model = load_model()
 
 # Load class indices 
 with open("./src/class_indices.json", "r") as f:
     class_indices = json.load(f)
 
-# Convert to index → class mapping
 class_names = list(class_indices.keys())
 
 
@@ -31,7 +24,7 @@ def run():
 
     st.title("Environment Image Classifier")
 
-    # Form input (consistent with your style)
+    # Form input 
     with st.form(key='form_image_classifier'):
         uploaded_file = st.file_uploader(
             "Upload an image",
@@ -72,7 +65,5 @@ def run():
         for i, prob in enumerate(prediction):
             st.write(f"{class_names[i]}: {prob:.2%}")
 
-
-# Run app
 if __name__ == '__main__':
     run()