Update app.py

app.py

@@ -8,6 +8,7 @@ import scipy
 import copy
 from PIL import Image
 import torch.nn as nn
+import pandas as pd
 from my_model.object_detection import detect_and_draw_objects
 from my_model.captioner.image_captioning import get_caption
 from my_model.gen_utilities import free_gpu_resources
@@ -29,13 +30,14 @@ sample_images = ["Files/sample1.jpg", "Files/sample2.jpg", "Files/sample3.jpg",
 
 
 def analyze_image(image, model):
-    st.text("Cool image, let me analyze it..")
-    caption = model.get_caption(image)
-    image_with_boxes, detected_objects_str = model.detect_objects(image)
+    
+    img = copy.deepcopy(image)  # we dont wanna apply changes to the original image
+    caption = model.get_caption(img)
+    image_with_boxes, detected_objects_str = model.detect_objects(img)
     st.text("I am ready, let's talk!")
     free_gpu_resources()
     
-    return caption, detected_objects_str
+    return caption, detected_objects_str, image_with_boxes
     
 
 def image_qa_app(kbvqa):
@@ -61,8 +63,9 @@ def image_qa_app(kbvqa):
     for image_key, image_data in st.session_state['images_data'].items():
         st.image(image_data['image'], caption=f'Uploaded Image: {image_key[-11:]}', use_column_width=True)
         if not image_data['analysis_done']:
+            st.text("Cool image, please click 'Analyze Image'..")
             if st.button('Analyze Image', key=f'analyze_{image_key}'):
-                caption, detected_objects_str = analyze_image(image_data['image'], kbvqa)
+                caption, detected_objects_str, image_with_boxes = analyze_image(image_data['image'], kbvqa)  # we can use the image_with_boxes later if we want to show it. 
                 image_data['caption'] = caption
                 image_data['detected_objects_str'] = detected_objects_str
                 image_data['analysis_done'] = True
@@ -98,7 +101,7 @@ def process_new_image(image_key, image, kbvqa):
 
 def run_inference():
     st.title("Run Inference")
-    st.write("Please note that this is not a general purpose model, it is specifically trained on OK-VQA dataset and is designed to give short answers to the given questions.")
+    st.write("Please note that this is not a general purpose model, it is specifically trained on OK-VQA dataset and is designed to give direct and short answers to the given questions.")
 
     method = st.selectbox(
         "Choose a method:",
@@ -165,6 +168,16 @@ def run_inference():
     else:
         st.write('Model is not ready for inference yet')
 
+    # Display model settings
+    if 'kbvqa' in st.session_state and st.session_state['kbvqa'] is not None:
+        model_settings = {
+            'Detection Model': st.session_state['model_settings']['detection_model'],
+            'Confidence Level': st.session_state['model_settings']['confidence_level']
+        }
+        st.write("### Current Model Settings:")
+        st.table(pd.DataFrame(model_settings, index=[0]))
+
+
 
             
 # Main function