| import gradio as gr |
| from src.pipeline.prediction_pipeline import PredictionPipeline |
| import numpy as np |
| from PIL import Image |
|
|
| pipeline = PredictionPipeline() |
|
|
| def predict_single(image): |
| |
| if image is None: |
| return None, "No image detected!", "No image detected!" |
| img = Image.fromarray(image) if isinstance(image, np.ndarray) else image |
| result = pipeline.predict(img) |
| annotated_img = pipeline.annotate(img, result) |
| return annotated_img, result["category"], result["freshness"] |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown("# Food Freshness Detection") |
|
|
| with gr.Tab("Image Upload"): |
| image = gr.Image(sources=["upload"], label="Upload an Image") |
| out_img = gr.Image() |
| cat = gr.Textbox(label="Category") |
| fresh = gr.Textbox(label="Freshness") |
| btn = gr.Button("Predict on Image") |
| btn.click(predict_single, inputs=image, outputs=[out_img, cat, fresh]) |
|
|
| |
| with gr.Tab("Live Webcam"): |
| webcam = gr.Image(sources=["webcam"], label="Webcam") |
| out_img = gr.Image() |
| cat = gr.Textbox(label="Category") |
| fresh = gr.Textbox(label="Freshness") |
| btn = gr.Button("Predict") |
| btn.click(predict_single, inputs=webcam, outputs=[out_img, cat, fresh]) |
|
|
|
|
| |
|
|
| if __name__ == "__main__": |
| demo.launch(server_name="0.0.0.0", server_port=7860) |
|
|
