Test updates

app.py

@@ -117,24 +117,26 @@ def predict(image, reader, processor: LayoutLMv3Processor, model: LayoutLMv3ForS
         predicted_class = logits.argmax()
         probabilities = F.softmax(logits, dim=-1).flatten().tolist()
         return predicted_class.detach().item(), probabilities
-    
-reader = create_ocr_reader()
-processor = create_processor()
-model = create_model()
-
-uploaded_file = st.file_uploader("Choose a JPG file", ["jpg", "png"])
-if uploaded_file is not None:
-    bytes_data = io.BytesIO(uploaded_file.read())    
-    image = Image.open(bytes_data)
-    st.image(image, caption="Uploaded Image", use_column_width=True)
-    predicted, probabilities = predict(image, reader, processor, model)
-    predicted_label = model.config.id2label[predicted]
-    st.markdown(f"Predicted Label: {predicted_label}")
-
-    df = pd.DataFrame({
-        "Label": list(model.config.id2label.values()),
-        "Probability": probabilities
-    })
-    fig = px.bar(df, x="Label", y="Probability")
-    st.plotly_chart(fig, use_container_width=True)
+
+st.markdown(f"Test")
+
+# reader = create_ocr_reader()
+# processor = create_processor()
+# model = create_model()
+
+# uploaded_file = st.file_uploader("Choose a JPG file", ["jpg", "png"])
+# if uploaded_file is not None:
+#     bytes_data = io.BytesIO(uploaded_file.read())    
+#     image = Image.open(bytes_data)
+#     st.image(image, caption="Uploaded Image", use_column_width=True)
+#     predicted, probabilities = predict(image, reader, processor, model)
+#     predicted_label = model.config.id2label[predicted]
+#     st.markdown(f"Predicted Label: {predicted_label}")
+
+#     df = pd.DataFrame({
+#         "Label": list(model.config.id2label.values()),
+#         "Probability": probabilities
+#     })
+#     fig = px.bar(df, x="Label", y="Probability")
+#     st.plotly_chart(fig, use_container_width=True)