Upload app.py

app.py

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+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)