Update app.py

app.py

@@ -1,61 +1,70 @@
-from helper.model import fetch_model, preprocess_image, classify_image
-import streamlit as st
-
-st.set_page_config(
-    page_title="VGNet Prototype",
-    page_icon="🤖",
-)
-
-st.title("Using Deep Learning Techniques on Philippine Corn Leaf Disease Classification")
-st.write("This is the prototype of VGNet model trained using different optimizers")
-st.write("Thesis Project by Group BDK of Mapua University")
-
-st.image(
-    "resources/corn-field.png",
-    caption='A corn field',
-    width=700
-)
-
-st.header("How to use?")
-st.markdown("""
-1. Select a model.
-2. Select an image (Leaf Blight, Gray Leaf Spot, Common Rust).
-3. Upload the selected image here.
-4. The model will output the classification result if the image is (Leaf Blight, Gray Leaf Spot, Common Rust).
-""")
-
-uploaded_image = st.file_uploader(
-    "Choose an image...",
-    type=["bmp", "png", "jpg", "jpeg"]
-)
-
-model_labels = [
-    "VGNet + Adam",
-    "VGNet + Adan",
-    "VGNet + AdamW",
-    "VGNet + RMSprop",
-    "VGNet + AdaBound"
-]
-model_option = st.selectbox(
-    "Select a Model:",
-    list(range(len(model_labels))),
-    format_func=lambda ind: model_labels[ind]
-)
-
-
-# Check if an image was uploaded
-if uploaded_image is not None:
-
-    # Preprocess the image
-    image = preprocess_image(uploaded_image)
-
-    # Fetch the model
-    model_optimizer_name = ["adam", "adan", "adamw", "rmsprop", "adabound"]
-    model = fetch_model(model_optimizer_name[model_option])
-
-    # Perform classification
-    predicted_label = classify_image(model, image)
-
-    # Display Prediction Result
-    st.image(image)
-    st.write(f"Prediction Results: {predicted_label}")
+from helper.model import fetch_model, preprocess_image, classify_image
+from PIL import Image
+import streamlit as st
+import io
+
+st.set_page_config(
+    page_title="VGNet Prototype",
+    page_icon="🤖",
+)
+
+st.title("Using Deep Learning Techniques on Philippine Corn Leaf Disease Classification")
+st.write("This is the prototype of VGNet model trained using different optimizers")
+st.write("Thesis Project by Group BDK of Mapua University")
+
+st.image(
+    "resources/corn-field.png",
+    caption='A corn field',
+    width=700
+)
+
+st.header("How to use?")
+st.markdown("""
+1. Select a model.
+2. Select an image (Leaf Blight, Gray Leaf Spot, Common Rust).
+3. Upload the selected image here.
+4. The model will output the classification result if the image is (Leaf Blight, Gray Leaf Spot, Common Rust).
+""")
+
+uploaded_image = st.file_uploader(
+    "Choose an image...",
+    type=["bmp", "png", "jpg", "jpeg"]
+)
+
+model_labels = [
+    "VGNet + Adam",
+    "VGNet + Adan",
+    "VGNet + AdamW",
+    "VGNet + RMSprop",
+    "VGNet + AdaBound"
+]
+model_option = st.selectbox(
+    "Select a Model:",
+    list(range(len(model_labels))),
+    format_func=lambda ind: model_labels[ind]
+)
+
+
+# Check if an image was uploaded
+if uploaded_image is not None:
+
+    # Preprocess the image
+    image = preprocess_image(
+        Image.open(
+            io.BytesIO(
+                uploaded_image
+            )
+        )
+
+    )
+
+    # Fetch the model
+    model_optimizer_name = ["adam", "adan", "adamw", "rmsprop", "adabound"]
+    model = fetch_model(model_optimizer_name[model_option])
+
+    # Perform classification
+    predicted_label = classify_image(model, image)
+
+    # Display Prediction Result
+    st.image(image)
+    st.write(f"Prediction Results: {predicted_label}")