Update app.py

app.py

@@ -1,13 +1,13 @@
 import streamlit as st
 from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.image import img_to_array  # Import this function
+from tensorflow.keras.preprocessing.image import img_to_array
 from PIL import Image
 import numpy as np
 import os
 from huggingface_hub import hf_hub_download, login
 
-# Title of the Streamlit app
-st.title("Yellow Rust Severity Prediction")
+# Set page configuration
+st.set_page_config(page_title="Yellow Rust Severity Prediction", layout="wide", initial_sidebar_state="expanded")
 
 # Authentication using Hugging Face token
 authkey = os.getenv('YellowRust')
@@ -21,66 +21,63 @@ loaded_model = load_model(model_path)
 
 # Function to preprocess the uploaded image
 def preprocess_image(image):
-    # Resize the image to match the model input size (e.g., 224x224 for many pre-trained models)
-    image = image.resize((224, 224))  # Adjust size based on your model input
+    image = image.resize((224, 224))  # Resize to match model input size
     image = img_to_array(image)       # Convert image to numpy array
     image = image / 255.0            # Normalize pixel values to [0, 1]
     image = np.expand_dims(image, axis=0)  # Add batch dimension
     return image
 
-# Streamlit file uploader
-uploaded_file = st.file_uploader("Upload a wheat leaf image", type=["jpg", "jpeg", "png"])
+# Sidebar layout with a colorful menu
+st.sidebar.markdown('<p style="font-size: 24px; color: #2F4F4F; font-weight: bold;">Yellow Rust Prediction</p>', unsafe_allow_html=True)
+st.sidebar.markdown('<p style="color: #555;">Upload an image of the wheat leaf to predict the severity of yellow rust.</p>', unsafe_allow_html=True)
+
+# Sidebar elements
+uploaded_file = st.sidebar.file_uploader("Upload Wheat Leaf Image", type=["jpg", "jpeg", "png"])
+st.sidebar.markdown("---")
+
+# Main content
+st.title("Yellow Rust Severity Prediction")
+st.markdown('<p style="text-align: center; color: #2F4F4F; font-size: 30px; font-weight: bold;">Yellow Rust Severity Prediction Dashboard</p>', unsafe_allow_html=True)
 
+# Display the uploaded image
 if uploaded_file is not None:
-    st.subheader("Uploaded Image")
     image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image", use_container_width=True, width=500)
+    st.image(image, caption="Uploaded Wheat Leaf", use_column_width=True)
 
     # Preprocess the image
     processed_image = preprocess_image(image)
 
-    # Perform prediction
+    # Predict severity with a spinner
     with st.spinner("Predicting..."):
         prediction = loaded_model.predict(processed_image)
         predicted_class = np.argmax(prediction, axis=1)[0]  # Get the class index
+        
         class_labels = ['Healthy', 'Mild Rust (MR)', 'Moderate Rust (MRMS)', 'Severe Rust (MS)', 'Very Severe Rust (R)', 'Extremely Severe Rust (S)']
-
-        st.header("Predicted Severity Class")
         
-        # Colorful headings and styled responses
+        st.header("Predicted Severity Class")
+
+        # Conditional statements for displaying the prediction with styled headers and colors
         if predicted_class == 0:
-            st.markdown('<p style="color: #28a745; font-size: 22px; font-weight: bold;">Healthy</p>', unsafe_allow_html=True)
+            st.markdown('<p style="color: green; font-size: 22px; font-weight: bold;">Healthy</p>', unsafe_allow_html=True)
             st.write("The leaf appears healthy. There is no immediate action required. Continue monitoring as needed.")
         elif predicted_class == 1:
-            st.markdown('<p style="color: #FFA500; font-size: 22px; font-weight: bold;">Mild Rust (MR)</p>', unsafe_allow_html=True)
-            st.write("Mild rust detected.")
-            st.write("1. Apply fungicides to control rust growth.")
-            st.write("2. Regularly monitor the leaf for further signs of infection.")
+            st.markdown('<p style="color: orange; font-size: 22px; font-weight: bold;">Mild Rust (MR)</p>', unsafe_allow_html=True)
+            st.write("Mild rust detected. Applying fungicides will help control further spread.")
         elif predicted_class == 2:
-            st.markdown('<p style="color: #FF6347; font-size: 22px; font-weight: bold;">Moderate Rust (MRMS)</p>', unsafe_allow_html=True)
-            st.write("Moderate rust detected.")
-            st.write("1. Continue monitoring the leaf for any progression.")
-            st.write("2. Treat with fungicides as required.")
+            st.markdown('<p style="color: #FFA500; font-size: 22px; font-weight: bold;">Moderate Rust (MRMS)</p>', unsafe_allow_html=True)
+            st.write("Moderate rust detected. Monitor regularly and treat with fungicides.")
         elif predicted_class == 3:
             st.markdown('<p style="color: #FF4500; font-size: 22px; font-weight: bold;">Severe Rust (MS)</p>', unsafe_allow_html=True)
-            st.write("Severe rust detected.")
-            st.write("1. Apply fungicides promptly to control rust spread.")
-            st.write("2. Ensure regular monitoring to prevent further spread.")
+            st.write("Severe rust detected. Prompt fungicide application and continued monitoring are recommended.")
         elif predicted_class == 4:
-            st.markdown('<p style="color: #dc3545; font-size: 22px; font-weight: bold;">Very Severe Rust (R)</p>', unsafe_allow_html=True)
-            st.write("Very severe rust detected.")
-            st.write("1. Implement intensive control measures.")
-            st.write("2. Apply fungicides multiple times.")
-            st.write("3. Frequent monitoring is essential.")
+            st.markdown('<p style="color: red; font-size: 22px; font-weight: bold;">Very Severe Rust (R)</p>', unsafe_allow_html=True)
+            st.write("Very severe rust detected. Intensive control measures and frequent monitoring are required.")
         elif predicted_class == 5:
-            st.markdown('<p style="color: #8B0000; font-size: 22px; font-weight: bold;">Extremely Severe Rust (S)</p>', unsafe_allow_html=True)
-            st.write("Extremely severe rust detected.")
-            st.write("1. Apply aggressive control strategies.")
-            st.write("2. Seek expert advice for advanced interventions.")
-            st.write("3. Frequent, close monitoring is critical.")
-
+            st.markdown('<p style="color: darkred; font-size: 22px; font-weight: bold;">Extremely Severe Rust (S)</p>', unsafe_allow_html=True)
+            st.write("Extremely severe rust detected. Apply aggressive control strategies and seek expert advice.")
+        
         confidence = np.max(prediction) * 100
-        st.markdown(f'<p style="color: #17a2b8; font-size: 18px;">**Confidence Level:** {confidence:.2f}%</p>', unsafe_allow_html=True)
+        st.markdown(f'<p style="color: #17a2b8; font-size: 18px; font-weight: bold;">Confidence Level: {confidence:.2f}%</p>', unsafe_allow_html=True)
 
 # Footer
-st.info("MPHIL Final Year Project By Mr. Asim Khattak")
+st.info("MPHIL Final Year Project By Mr. Asim Khattak", icon="📚")