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Cassava_Disease_Classifier

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Update app.py

e905d51 verified over 1 year ago

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# Available backend options are: "jax", "torch", "tensorflow".
import os
#os.environ["KERAS_BACKEND"] = "tensorflow"
#pip install -U keras huggingface_hub

#import keras

#model = keras.saving.load_model("hf://Fluospark128/Cassava_Disease_Classifier")

import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image

# Load the model
#@st.cache_resource
def load_model():
    model = tf.keras.models.load_model("cassava_leaf_disease_model.keras") #tf.keras.saving.load_model("hf://Fluospark128/Cassava_Disease_Classifier") cassava_model.h5
    return model

model = load_model()

# Class labels
CLASS_NAMES = ["Cassava Bacterial Blight Disease", "Cassava Brown Streak Disease", "Cassava Green Mottle Disease",  "Cassava Mosaic Disease", "Healthy"]

# Streamlit UI
st.title("Cassava Leaf Disease Identifier")
st.write("Upload an image of a cassava leaf to identify its disease.")

# File uploader
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])

if uploaded_file is not None:
    image = Image.open(uploaded_file).resize((224, 224))
    st.image(image, caption="Uploaded Image", use_container_width=True)

    # Preprocess image
    img_array = np.array(image) / 255.0  # Normalize
    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension

    # Make prediction
    prediction = model.predict(img_array)
    predicted_class = CLASS_NAMES[np.argmax(prediction)]

    st.write(f"Prediction: **{predicted_class}**")

	# Available backend options are: "jax", "torch", "tensorflow".
	import os
	#os.environ["KERAS_BACKEND"] = "tensorflow"
	#pip install -U keras huggingface_hub

	#import keras

	#model = keras.saving.load_model("hf://Fluospark128/Cassava_Disease_Classifier")

	import streamlit as st
	import tensorflow as tf
	import numpy as np
	from PIL import Image

	# Load the model
	#@st.cache_resource
	def load_model():
	model = tf.keras.models.load_model("cassava_leaf_disease_model.keras") #tf.keras.saving.load_model("hf://Fluospark128/Cassava_Disease_Classifier") cassava_model.h5
	return model

	model = load_model()

	# Class labels
	CLASS_NAMES = ["Cassava Bacterial Blight Disease", "Cassava Brown Streak Disease", "Cassava Green Mottle Disease", "Cassava Mosaic Disease", "Healthy"]

	# Streamlit UI
	st.title("Cassava Leaf Disease Identifier")
	st.write("Upload an image of a cassava leaf to identify its disease.")

	# File uploader
	uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])

	if uploaded_file is not None:
	image = Image.open(uploaded_file).resize((224, 224))
	st.image(image, caption="Uploaded Image", use_container_width=True)

	# Preprocess image
	img_array = np.array(image) / 255.0 # Normalize
	img_array = np.expand_dims(img_array, axis=0) # Add batch dimension

	# Make prediction
	prediction = model.predict(img_array)
	predicted_class = CLASS_NAMES[np.argmax(prediction)]

	st.write(f"Prediction: {predicted_class}")