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import gradio as gr
import warnings
import src.utils as utils
from src.predict import prediction
from src.logger import get_logger
logger = get_logger(__name__)
warnings.filterwarnings("ignore")
# utils.copy_builder()
def show_processing_text():
return gr.update(visible=True), gr.update(visible=False)
def prediction_with_loading(image, longitude, latitude, cloud_cover, evapotranspiration, precipitation, min_temp, mean_temp, max_temp, vapour_pressure, wet_day_freq):
try:
logger.info("Starting prediction process...")
response = prediction(
image, longitude, latitude, cloud_cover, evapotranspiration,
precipitation, min_temp, mean_temp, max_temp, vapour_pressure, wet_day_freq
)
logger.info("Prediction completed successfully.")
return gr.update(value=response, visible=True), gr.update(visible=False)
except Exception as e:
logger.error(f"Error in prediction: {str(e)}")
return "An error occurred during prediction. Please try again.", gr.update(visible=False)
with gr.Blocks(css=utils.css, js=utils.js, theme=gr.themes.Ocean(font=gr.themes.GoogleFont("Poppins"), primary_hue=gr.themes.colors.red, secondary_hue=gr.themes.colors.pink)) as demo:
gr.Markdown("## LUMPY SKIN DISEASE PREDICTION", elem_classes="title")
with gr.Row():
with gr.Column(scale=5):
# Image upload
image_input = gr.Image(type="pil", label="Upload Image", height=177, elem_classes="image-input")
with gr.Column(scale=5):
longitude = gr.Number(label="Longitude")
latitude = gr.Number(label="Latitude")
with gr.Row():
with gr.Column(scale=5):
cloud_cover = gr.Number(label="Monthly Cloud Cover", elem_classes="num-input")
evapotranspiration = gr.Number(label="Potential EvapoTranspiration", elem_classes="num-input")
with gr.Column(scale=5):
precipitation = gr.Number(label="Precipitation", elem_classes="num-input")
min_temp = gr.Number(label="Minimum Temperature", elem_classes="num-input")
with gr.Column(scale=5):
mean_temp = gr.Number(label="Mean Temperature", elem_classes="num-input")
max_temp = gr.Number(label="Maximum Temperature", elem_classes="num-input")
with gr.Column(scale=5):
vapour_pressure = gr.Number(label="Vapour Pressure", elem_classes="num-input")
wet_day_freq = gr.Number(label="Wet Day Frequency", elem_classes="num-input")
with gr.Row():
predict_button = gr.Button("Predict", variant="primary")
processing_text = gr.Markdown("", visible=False, height=100)
output_text = gr.Markdown(label="LLM Generated Diagnostic Report", container=True, show_copy_button=True, visible=False)
predict_button.click(
fn=show_processing_text,
inputs=[],
outputs=[processing_text, output_text],
queue=False
)
predict_button.click(
fn=prediction_with_loading,
inputs=[image_input, longitude, latitude, cloud_cover, evapotranspiration, precipitation, min_temp, mean_temp, max_temp, vapour_pressure, wet_day_freq],
outputs=[output_text, processing_text],
queue=True
)
demo.launch(share=True)
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