Upload 4 files

app.py

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+import streamlit as st 
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+
+model = load_model("model_grapevine_disease_detection.h5")
+
+def process_image(img):
+    img = img.convert("RGB")
+    img = img.resize((50,50))
+    img = np.array(img)
+    if img.ndim == 2:
+        img = np.stack((img,)*3, axis=-1)
+    img = img/255.0
+    img = np.expand_dims(img, axis=0)
+    return img
+
+st.title("GRAPEVINE DISEASE CLASSIFICATION")
+st.divider()
+
+col1, col2, col3 = st.columns([1,2,1])
+with col2:
+    st.image("grapevine_disease.jpeg")
+st.divider()
+
+st.success("Upload your grapevine image and classify the images with the following labels: Black Rot, ESCA, Healthy, and Leaf Blight with CNN deep learning.")
+st.divider()
+
+st.write("Upload your image and see the results")
+st.divider()
+
+file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png", "webp"])
+
+if file is not None:
+    img = Image.open(file)
+    st.image(img, caption="Downloaded image")
+    image = process_image(img)
+    prediction = model.predict(image)
+    predicted_class = np.argmax(prediction)
+    
+    class_names = {0:"Black Rot", 1:"ESCA", 2:"Healthy", 3:"Leaf Blight"}
+    
+    st.write(f"Predicted Grapevine Disease: {class_names[predicted_class]}")

model_grapevine_disease_detection.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:73e6bed46d2e30c75f1e87fa5b712d34547ad8f8860d56ecd05068ff94806988
+size 64980992

requirements.txt

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+streamlit
+scikit-learn