Update app.py

app.py

@@ -1,39 +1,63 @@
-import streamlit as st
+from tensorflow import keras
 from PIL import Image, ImageOps
-import io
-from img_classification import teachable_machine_classification, load_model
-
+import numpy as np
+import io, os
+import logging
+import keras_metrics
 from tensorflow import keras
+import utils
+## Configs
+keras.utils.get_custom_objects()['recall'] = utils.recall
+keras.utils.get_custom_objects()['precision'] = utils.precision
+keras.utils.get_custom_objects()['f1'] = utils.f1
+
+
+def teachable_machine_classification(img=None, model=None):
+    """Performs inference on image uploaded"""
+
+    # Create the array of the right shape to feed into the keras model
+    data = np.ndarray(shape=(1, 299, 299, 3), dtype=np.float32)
+    image = img
+    # image sizing
+    size = (299, 299)
+    image = ImageOps.fit(image, size, Image.ANTIALIAS)
+
+    # turn the image into a numpy array
+    image_array = np.asarray(image)
+
+    # Normalize the image
+    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+
+    # Load the image into the array
+    data[0] = normalized_image_array
+
+    # run the inference
+    prediction = model.predict(data)
+
+    print("Prediction", prediction)
+    return prediction[0][
+        1
+    ]  # np.argmax(prediction) # return position of the highest probability
+
+
+def load_model(weights_file=None):
+    """Loads trained keras model"""
+    dependencies = {
+        "binary_f1_score": keras_metrics.binary_f1_score,
+        "binary_precision": keras_metrics.binary_precision,
+        "binary_recall": keras_metrics.binary_recall,
+    }
+
+    try:
+        assert os.path.exists(weights_file), f"File '{weights_file}' does not exist"
+        # Load the model
+        model = keras.models.load_model(
+            weights_file, custom_objects=dependencies, compile=False
+        )
 
+        return model
+    except Exception as e:
+        logging.error("ERROR: ", e)
+        print("ERROR: ", e, " Failed to load ML model")
 
-st.set_option("deprecation.showfileUploaderEncoding", False)
-
-st.title("Detecting presence of Poison Oak")
-st.header("Poison Oak Classification Example")
-st.text("Upload an image for classification as poison oak or no poison oak")
-
-
-# Load trained model
-model = load_model("./best_model.h5")
-
-print("Starting Streamlit app")
-uploaded_file = st.file_uploader("Select an image ...", type=["jpg", "png", "jpeg"])
-
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded image", use_column_width=True)
-    st.write("")
-    st.write("Classifying...")
-    label = teachable_machine_classification(img=image, model=model)
-    if label <= 0.2:
-        st.write("Very unlikely that this is poison oak.")
-    elif (label > 0.2) & (label <= 0.6):
-        st.write(
-            "Unsure from this picture. You may need to retake a closer/clearer picture."
-        )
-    elif (label > 0.6) & (label <= 0.7):
-        st.write("Decent chance that this is poison oak.")
-    else:
-        st.write("{:.1f}% chance that this might be poison oak".format(label * 100))
-else:
-    st.write("No file uploaded")
+        return None