Update app.py

app.py

@@ -5,12 +5,27 @@ import accelerate
 import scipy
 from PIL import Image
 import torch.nn as nn
-from transformers import Blip2Processor, Blip2ForConditionalGeneration, InstructBlipProcessor, InstructBlipForConditionalGeneration
 from my_model.object_detection import detect_and_draw_objects
 from my_model.captioner.image_captioning import get_caption
 from my_model.utilities import free_gpu_resources
 
 
+def perform_object_detection(image, model_name, threshold=0.2):
+    """
+    Perform object detection on the given image using the specified model and threshold.
+    Args:
+    image (PIL.Image): The image on which to perform object detection.
+    model_name (str): The name of the object detection model to use.
+    threshold (float): The threshold for object detection.
+    Returns:
+    PIL.Image, str: The image with drawn bounding boxes and a string of detected objects.
+    """
+
+    processed_image, detected_objects = detect_and_draw_objects(image, model_name, threshold)
+
+    return processed_image, detected_objects
+
+    
 # Placeholder for undefined functions
 def load_caption_model():
     st.write("Placeholder for load_caption_model function")
@@ -20,7 +35,7 @@ def answer_question(image, question, model, processor):
     return "Placeholder answer for the question"
 
 def detect_and_draw_objects(image, model_name, threshold):
-    return image, "Detected objects"
+    perform_object_detection()
 
 def get_caption(image):
     return "Generated caption for the image"
@@ -94,12 +109,6 @@ def image_qa_app():
         st.session_state['images_qa_history'] = []
         st.experimental_rerun()
 
-    # Display sample images
-    st.write("Or choose from sample images:")
-    for idx, sample_image_path in enumerate(sample_images):
-        if st.button(f"Use Sample Image {idx+1}", key=f"sample_{idx}"):
-            uploaded_image = Image.open(sample_image_path)
-            process_uploaded_image(uploaded_image)
 
     # Image uploader
     uploaded_image = st.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"])
@@ -107,6 +116,13 @@ def image_qa_app():
         image = Image.open(uploaded_image)
         process_uploaded_image(image)
 
+    # Display sample images
+    st.write("Or choose from sample images:")
+    for idx, sample_image_path in enumerate(sample_images):
+        if st.button(f"Use Sample Image {idx+1}", key=f"sample_{idx}"):
+            uploaded_image = Image.open(sample_image_path)
+            process_uploaded_image(uploaded_image)
+
 def process_uploaded_image(image):
     current_image_key = image.filename  # Use image filename as a unique key
     # ... rest of the image processing code ...