Update app.py

app.py

@@ -37,47 +37,5 @@ with gr.Blocks() as demo:
 
     
 
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
-=======
-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)