Update app.py

app.py

@@ -83,24 +83,12 @@ input_character_character_matching_threshold = st.sidebar.slider('Character-char
 input_text_character_matching_threshold = st.sidebar.slider('Text-character matching threshold', 0.0, 1.0, 0.4, step=0.01)
 
 
-if path_to_image is None:
-    path_to_image = "https://i.imgur.com/pZqPlSR.jpeg"
-
-image = read_image_as_np_array(path_to_image)
-
-st.markdown("**Prediction**")
-if generate_detections_and_associations or generate_transcript:
-    result = predict_detections_and_associations(
-    path_to_image,
-    input_character_detection_threshold,
-    input_panel_detection_threshold,
-    input_text_detection_threshold,
-    input_character_character_matching_threshold,
-    input_text_character_matching_threshold,
-)
-
-if generate_transcript:
-    ocr_results = predict_ocr(
+if path_to_image is not None:
+    image = read_image_as_np_array(path_to_image)
+    
+    st.markdown("**Prediction**")
+    if generate_detections_and_associations or generate_transcript:
+        result = predict_detections_and_associations(
         path_to_image,
         input_character_detection_threshold,
         input_panel_detection_threshold,
@@ -108,21 +96,31 @@ if generate_transcript:
         input_character_character_matching_threshold,
         input_text_character_matching_threshold,
     )
-
-if generate_detections_and_associations and generate_transcript:
-    col1, col2 = st.columns(2)
-    output = model.visualise_single_image_prediction(image, result)
-    col1.image(output)
-    text_bboxes_for_all_images = [result["texts"]]
-    ocr_results = model.predict_ocr([image], text_bboxes_for_all_images)
-    transcript = model.generate_transcript_for_single_image(result, ocr_results[0])
-    col2.text(transcript)
-
-elif generate_detections_and_associations:
-    output = model.visualise_single_image_prediction(image, result)
-    st.image(output)
-
-elif generate_transcript:
-    transcript = model.generate_transcript_for_single_image(result, ocr_results[0])
-    st.text(transcript)
+    
+    if generate_transcript:
+        ocr_results = predict_ocr(
+            path_to_image,
+            input_character_detection_threshold,
+            input_panel_detection_threshold,
+            input_text_detection_threshold,
+            input_character_character_matching_threshold,
+            input_text_character_matching_threshold,
+        )
+    
+    if generate_detections_and_associations and generate_transcript:
+        col1, col2 = st.columns(2)
+        output = model.visualise_single_image_prediction(image, result)
+        col1.image(output)
+        text_bboxes_for_all_images = [result["texts"]]
+        ocr_results = model.predict_ocr([image], text_bboxes_for_all_images)
+        transcript = model.generate_transcript_for_single_image(result, ocr_results[0])
+        col2.text(transcript)
+    
+    elif generate_detections_and_associations:
+        output = model.visualise_single_image_prediction(image, result)
+        st.image(output)
+    
+    elif generate_transcript:
+        transcript = model.generate_transcript_for_single_image(result, ocr_results[0])
+        st.text(transcript)