import gradio as gr
from PIL import Image
import os
from plant_disease_classifier import PlantDiseaseClassifier

# Directory containing test images
TEST_IMAGE_DIR = "test"

# Define model paths and types
model_types = ["resnet", "vit", "levit"]
model_paths = {
    "resnet": "resnet50_ft.pth",
    "vit": "vit32b_ft.pth",
    "levit": "levit128s_ft.pth",
}
classifiers = {
    name: PlantDiseaseClassifier(model_type, model_path)
    for name, model_type, model_path in zip(model_paths.keys(), model_types, model_paths.values())
}

# List all test images
def get_test_images():
    return [f for f in os.listdir(TEST_IMAGE_DIR) if f.lower().endswith(('.jpg', '.png'))]

def predict(image, model_name):
    classifier = classifiers[model_name]
    predicted_class = classifier.predict(image)
    return predicted_class

def classify_uploaded_image(image, model_name):
    return predict(image, model_name)

def classify_preloaded_image(image_name, model_name):
    image_path = os.path.join(TEST_IMAGE_DIR, image_name)
    image = Image.open(image_path).convert("RGB")
    return predict(image, model_name)

model_choices = list(model_paths.keys())
test_images = get_test_images()

# Define Gradio app
with gr.Blocks() as demo:
    gr.Markdown("# Plant Disease Classifier")

    with gr.Tab("Upload an Image"):
        with gr.Row():
            image_input = gr.Image(type="pil", label="Upload an Image")
            model_input_upload = gr.Dropdown(choices=model_choices, label="Select Model", value="resnet")
        classify_button_upload = gr.Button("Classify")
        output_text_upload = gr.Textbox(label="Predicted Class")
        classify_button_upload.click(classify_uploaded_image, inputs=[image_input, model_input_upload], outputs=output_text_upload)

    with gr.Tab("Select a Preloaded Image"):
        with gr.Row():
            image_dropdown = gr.Dropdown(choices=test_images, label="Select a Test Image")
            model_input_preloaded = gr.Dropdown(choices=model_choices, label="Select Model", value="resnet")
        classify_button_preloaded = gr.Button("Classify")
        output_text_preloaded = gr.Textbox(label="Predicted Class")
        classify_button_preloaded.click(classify_preloaded_image, inputs=[image_dropdown, model_input_preloaded], outputs=output_text_preloaded)

demo.launch()