Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from PIL import Image, ImageDraw
+
+# Load your trained meta-model
+MODEL_PATH = "hf://shaheer-data/Yellow-Rust-Prediction/final_meta_model.keras"  # Replace with your model path
+model = tf.keras.models.load_model(MODEL_PATH)
+
+# Set page configuration
+st.set_page_config(page_title="Yellow Rust Disease Classification", layout="wide", initial_sidebar_state="expanded")
+
+# Theme selection
+st.sidebar.title("Settings and Preferences")
+theme = st.sidebar.selectbox("Select Theme", ["Light", "Dark"])
+if theme == "Dark":
+    st.markdown(
+        "<style>body { background-color: #0e1117; color: white; }</style>",
+        unsafe_allow_html=True,
+    )
+
+# Title
+st.title("Yellow Rust Disease Classification Dashboard")
+
+# User Input Section
+st.sidebar.header("Upload Image of Plant Leaf")
+image_file = st.sidebar.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])
+
+if image_file:
+    st.subheader("Uploaded Image")
+    image = Image.open(image_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocess the image for prediction
+    def preprocess_image(img):
+        img = img.resize((224, 224))  # Adjust to model input size
+        img_array = np.array(img) / 255.0  # Normalize
+        return np.expand_dims(img_array, axis=0)  # Add batch dimension
+
+    processed_image = preprocess_image(image)
+
+    # Prediction Results
+    predictions = model.predict(processed_image)
+    class_names = ["0", "MR", "MRMS", "MS", "R", "S"]  # Replace with actual class labels from dataset
+    predicted_class = class_names[np.argmax(predictions)]
+    confidence = np.max(predictions) * 100
+
+    st.header("Prediction Results")
+    st.write(f"**Predicted Status:** {predicted_class}")
+    st.write(f"**Confidence Level:** {confidence:.2f}%")
+
+    # Severity Level
+    severity = "High" if confidence > 80 else "Moderate" if confidence > 50 else "Low"
+    st.write(f"**Severity Level:** {severity}")
+
+    # Highlighting a region (Example: bounding box)
+    st.subheader("Highlighted Regions")
+    draw = ImageDraw.Draw(image)
+    # Example bounding box coordinates, adjust as necessary
+    draw.rectangle([50, 50, 150, 150], outline="red", width=3)  # Example bounding box
+    st.image(image, caption="Highlighted Regions", use_column_width=True)
+
+    # Disease Insights
+    st.header("Disease Insights")
+    for i, class_name in enumerate(class_names):
+        st.write(f"{class_name}: {predictions[0][i] * 100:.2f}%")
+
+    # Model Performance Metrics (Admin Only)
+    if st.checkbox("Show Model Performance Metrics (Admin Only)"):
+        st.write("**Accuracy:** 95.6%")  # Replace with actual metric
+        st.write("**Precision:** 94.7%")  # Replace with actual metric
+        st.write("**Recall:** 93.5%")  # Replace with actual metric
+
+    # User Notifications
+    if severity == "High":
+        st.warning("High severity detected! Immediate action is recommended.")
+
+# Footer
+st.sidebar.info("Powered by Streamlit and TensorFlow")