Update app_utils.py

app_utils.py

@@ -1,17 +1,20 @@
 """
 Utility functions for the Instagram Caption Generator app.
 """
-import streamlit as st
-from dotenv import load_dotenv
-from transformers import AutoProcessor, Blip2ForConditionalGeneration
 import os
 from pathlib import Path
+
+from dotenv import load_dotenv
 import pandas as pd
+import streamlit as st
+from transformers import AutoProcessor, Blip2ForConditionalGeneration
 
 
 def get_gemini_api_key():
     """
-    Retrieves the Google API key for accessing the Generative AI API.
+    The api key is stored in as a private environment variable,
+    the purpose of this function is to retrieve the Google API key
+    for accessing the Generative AI API.
     :return: str - The Google API key.
     """
     load_dotenv()
@@ -20,24 +23,21 @@ def get_gemini_api_key():
 
 
 @st.cache_resource()
-def init_model(init_model_required):
+def init_model():
     """
     Initializes the BLIP-2 model and processor for image captioning.
-    This helper function allows for lazy loading of the model and processor.
+    The cache_resource decorator is used to cache the model and processor.
     The streamlit app can call this function to load the model and processor
-    only when needed.
-    :param init_model_required: bo
-    ol - Flag to indicate if the model needs to be initialized.
+    without reinitializing it.
+    :param init_model_required: bool - Flag to indicate if the model needs to be initialized.
     :returns: AutoProcessor, Blip2ForConditionalGeneration, bool - Model processor, BLIP-2 model, and flag.
     """
-    if init_model_required:
-        try:
-            processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
-            blip2_model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b")
-            init_model_required = False
-            return processor, blip2_model, init_model_required
-        except Exception as e:
-            st.error(f"Error occurred during model initialization: {e}")
+    try:
+        processor = AutoProcessor.from_pretrained(os.path.expanduser('~/data/pretrained/blip2-opt-2.7b'))
+        blip2_model = Blip2ForConditionalGeneration.from_pretrained(os.path.expanduser('~/data/pretrained/blip2-opt-2.7b'))
+        return processor, blip2_model
+    except Exception as e:
+        st.error(f"Error occurred during model initialization: {e}")
 
 
 # Function to store the user data to a CSV file
@@ -57,24 +57,27 @@ def save_user_data(first_name, last_name, email, phone):
         df = pd.read_csv(csv_file)
     else:
         df = pd.DataFrame(columns=["First Name", "Last Name", "Email", "Phone Number"])
-
-    # Add and save new user data (not for production).
-    new_data = pd.DataFrame({"First Name": [first_name],
-                             "Last Name": [last_name],
-                             "Email": [email],
-                             "Phone Number": [phone]})
-    df = pd.concat([df, new_data], ignore_index=True)
+    new_data = {"First Name": first_name, "Last Name": last_name, "Email": email, "Phone Number": phone}
+    df = df.append(new_data, ignore_index=True)
     df.to_csv(csv_file, index=False)
     return None
 
 
 def get_gif(path):
-    """Function to get the GIF image"""
+    """
+    Function to get the GIF image from the specified path.
+    :param path: str - Path to the GIF image
+    :return: bytes - The GIF image
+    """
     with open(path, "rb") as file:
         gif = file.read()
     return gif
 
 
 # Blip-2 does most of the standard image processing needed for image captioning.
-def process_image(image_data, processor):
+def process_image():
+    """
+    Unused function for image processing,
+    not needed for the current implementation.
+    """
     pass