Update team member images and refactor progress simulation function

app.py

@@ -119,9 +119,9 @@ ocr = PaddleOCR(use_angle_cls=True, lang='en')
 # Team details
 team_members = [
     {"name": "Aman Deep", "image": "aman.jpg"},  # Replace with actual paths to images
-    {"name": "Nandini", "image": "myimage.jpg"},
-    {"name": "Abhay Sharma", "image": "gaurav.jpg"},
-    {"name": "Ratan Prakash Mishra", "image": "anandimg.jpg"}
+    {"name": "Nandini", "image": "nandini.jpg"},
+    {"name": "Abhay Sharma", "image": "abhay.jpg"},
+    {"name": "Ratan Prakash Mishra", "image": "ratan.jpg"}
 ]
 
 # Function to preprocess the images for the model
@@ -160,10 +160,6 @@ def preprocess_image(image):
         print(f"Error processing image: {e}")
         return None  # Return None if there's an error
 
-# Function to check if a file exists
-def file_exists(file_path):
-    return os.path.isfile(file_path)
-
 # Function to display circular images in a matrix format
 def display_images_in_grid(images, max_images_per_row=4):
     num_images = len(images)
@@ -190,6 +186,13 @@ def display_team_members(members, max_members_per_row=4):
                 st.image(circular_img, use_column_width=True)  # Display the circular image
                 st.write(member["name"])  # Display the name below the image
 
+# Function to simulate loading process with a progress bar
+def simulate_progress():
+    progress_bar = st.progress(0)
+    for percent_complete in range(100):
+        time.sleep(0.02)
+        progress_bar.progress(percent_complete + 1)
+
 # Title and description
 st.title("Amazon Smbhav")
 # Team Details with links
@@ -309,7 +312,6 @@ elif app_mode == "Task 1":
 
     if post_url:
         caption, imageArray = get_instagram_post_details(post_url)
-        
         if caption or imageArray.shape[0] > 0:
             st.subheader("Caption:")
             st.write(caption)
@@ -330,6 +332,7 @@ elif app_mode == "Task 1":
             message = f"image ocr: {text} Caption: {caption}"
             st.write(message)
             #OCR result text to be parsed here through LLM and get product listing content.
+            simulate_progress()
             messages.append({"role": "user", "content": message})
             completion = client.chat.completions.create(
                 model="meta-llama/Llama-3.2-3B-Instruct", 
@@ -357,14 +360,6 @@ elif app_mode == "Task 1":
         # Display the circular images in a matrix/grid format
         display_images_in_grid(circular_images, max_images_per_row=4)
 
-        # Function to simulate loading process with a progress bar
-        def simulate_progress():
-            progress_bar = st.progress(0)
-            for percent_complete in range(100):
-                time.sleep(0.02)
-                progress_bar.progress(percent_complete + 1)
-        # Function to remove gibberish using regex (removes non-alphanumeric chars, filters out very short text)
-            
         if st.button("Start Analysis"):
             simulate_progress()
             # Loop through each uploaded image and process them
@@ -387,6 +382,7 @@ elif app_mode == "Task 1":
                     text += currText + " "
                 st.write(f"OCR Result: {text}")
                 #OCR result text to be parsed here through LLM and get product listing content.
+                simulate_progress()
                 messages.append({"role": "user", "content": text})
                 completion = client.chat.completions.create(
                     model="meta-llama/Llama-3.2-3B-Instruct",