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README.md

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----
-title: Prototype
-emoji: 🐨
-colorFrom: red
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Prototype
+emoji: 🐨
+colorFrom: red
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.39.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+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}")

requirements.txt

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+keras==2.15.0
+tensorflow==2.15.0
+gunicorn
+flask
+pillow
+numpy
+streamlit