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import gradio as gr
import numpy as np
import huggingface_hub
import onnxruntime as ort
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.inception_v3 import preprocess_input
from PIL import Image
# Define the model loading function
def load_model():
# Download the model from Hugging Face Hub
path = huggingface_hub.hf_hub_download(repo_id="srijonashraf/maize-leaf-disease-detection", filename="best_model.keras")
# Set session options
options = ort.SessionOptions()
options.intra_op_num_threads = 8
options.inter_op_num_threads = 8
# Create InferenceSession with ONNX Runtime
session = ort.InferenceSession(path, sess_options=options, providers=["CPUExecutionProvider", "CUDAExecutionProvider"])
return session
# Load the model
model = load_model()
# Define the image size your model expects
IMG_SIZE = (224, 224)
# Define class names
class_names = [
'Corn___Common_Rust',
'Corn___Gray_Leaf_Spot',
'Corn___Healthy',
'Corn___Northern_Leaf_Blight',
'Corn___Northern_Leaf_Spot',
'Corn___Phaeosphaeria_Leaf_Spot'
]
# Define prediction function
def predict(image):
img = Image.fromarray(np.uint8(image)).resize(IMG_SIZE)
img_array = preprocess_input(np.expand_dims(img, axis=0).astype(np.float32))
# Run inference
predictions = model.run(None, {"input": img_array})[0]
predicted_class = np.argmax(predictions)
confidence = np.max(predictions)
if confidence <= 0.8:
return "Unknown Object"
else:
return {class_names[predicted_class]: float(confidence)}
# Create Gradio interface
interface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=6),
title="Maize Leaf Disease Detection",
description="Upload an image of a maize leaf to classify its disease."
)
# Launch the interface
if __name__ == "__main__":
interface.launch() |