Create app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+%
+def single_image(image,mode):
+    image = image.resize((128, 128))
+    if mode=="rgb":
+        return np.array(img).flatten()
+    elif mode=="hsv":
+        return np.array(img.convert('HSV')).flatten()
+    else
+        kmeans = sio.load('kmeans.skops')
+        sift = cv2.SIFT_create()
+        img = img.convert("L")
+        keypoints, descriptors = sift.detectAndCompute(img, None)
+        words = kmeans.predict(descriptors)
+        hist, _ = np.histogram(words, bins=num_clusters, range=(0, num_clusters))
+        return np.array(hist)
+def Classify(img, pre,model):
+start_time = time.time() 
+image = PIL.Image.open(img)
+file=model+'_'+pre+'.skops'
+loaded_model = sio.load(file)
+predictions = loaded_model.predict(single_image(image,pre))
+end_time = time.time()  # Record end time
+elapsed_time_microseconds = (end_time - start_time) * 1_000_000_000  # Calculate elapsed time in microseconds
+
+    return f"{elapsed_time_microseconds/factor:.2f} ns",f"{elapsed_time_microseconds/1000:.2f} μs",factor, result
+    return result
+
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=Classify,  # Reference to the function
+    inputs=[
+       gr.Image(type="filepath"),
+        gr.Radio(
+            ["rgb", "hsv", "BoVW"],
+            label="Preprocessing",
+            info="Choose one" 
+        )  
+        # Input for sorting methodgr.Radio(
+            ["DT", "RF", "XGBoost"],
+            label="ML Model",
+            info="Choose one"  
+        ) 
+    ],
+    outputs=[
+            gr.Textbox(label="Class"),
+          gr.Textbox(label="Time")
+
+    ]
+)
+
+# Launch the interface
+interface.launch()