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# app.py
import streamlit as st
import tensorflow as tf
from PIL import Image
import numpy as np
# Set page config
st.set_page_config(page_title="Rice Disease Classifier", page_icon="🌾")
# Constants from your training
IMG_SIZE = (224, 224)
CLASS_NAMES = ['Bacterial_leaf_blight', 'Brown_spot', 'Healthy', 'Leaf_blast']
# Cache the model loading
@st.cache_resource
def load_model():
return tf.keras.models.load_model('rice_disease_model.keras')
# Load model
try:
model = load_model()
except Exception as e:
st.error(f"Error loading model: {str(e)}")
st.stop()
# Preprocessing function
def preprocess_image(image):
image = image.resize(IMG_SIZE)
img_array = tf.keras.utils.img_to_array(image)
img_array = tf.expand_dims(img_array, 0) # Create batch axis
img_array = tf.keras.applications.mobilenet_v2.preprocess_input(img_array)
return img_array
# Streamlit interface
st.title("Rice Disease Classifier 🌾")
st.write("Upload an image of a rice leaf for disease diagnosis")
uploaded_file = st.file_uploader("Choose an image...",
type=["jpg", "jpeg", "png", "webp"])
if uploaded_file is not None:
try:
# Read and display image
image = Image.open(uploaded_file).convert('RGB')
st.image(image, caption="Uploaded Image", use_container_width=True) # Fixed parameter here
# Preprocess and predict
with st.spinner('Analyzing...'):
processed_image = preprocess_image(image)
predictions = model.predict(processed_image)
predicted_class = CLASS_NAMES[np.argmax(predictions[0])]
confidence = np.max(predictions[0]) * 100
# Display results
st.subheader("Results")
st.success(f"Predicted Disease: **{predicted_class}**")
st.info(f"Confidence: **{confidence:.2f}%**")
# Show probability distribution
st.subheader("Class Probabilities")
for class_name, prob in zip(CLASS_NAMES, predictions[0]):
st.progress(float(prob), text=f"{class_name}: {prob*100:.2f}%")
except Exception as e:
st.error(f"Error processing image: {str(e)}") |