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