Enhance conversation function with detailed comments for better clarity and maintainability

app.py

@@ -204,6 +204,7 @@ def conversation(
     hf_token,
     model_path,
 ):
+    # Initialize LLM
     if hf_token.strip() != "" and model_path.strip() != "":
         llm = HuggingFaceEndpoint(
             repo_id=model_path,
@@ -219,6 +220,7 @@ def conversation(
             huggingfacehub_api_token=os.getenv("P_HF_TOKEN", "None"),
         )
 
+    # Get vector database collections
     text_collection = vectordb_client.get_collection(
         "text_db", embedding_function=sentence_transformer_ef
     )
@@ -226,6 +228,7 @@ def conversation(
         "image_db", embedding_function=sentence_transformer_ef
     )
 
+    # Query text and image collections
     results = text_collection.query(
         query_texts=[msg], include=["documents"], n_results=num_context
     )["documents"][0]
@@ -236,9 +239,9 @@ def conversation(
         n_results=img_context,
     )
 
+    # Process similar images
     img_links = similar_images["metadatas"][0] if similar_images["metadatas"] else []
     images_and_locs = []
-
     for distance, link in zip(similar_images["distances"][0], img_links):
         try:
             img = Image.open(io.BytesIO(base64.b64decode(link["image"])))
@@ -247,12 +250,14 @@ def conversation(
         except Exception as e:
             print(f"Error decoding image: {e}")
 
+    # Fallback to placeholder if no images are found
     if not images_and_locs:
-        placeholder_path = "assets/placeholder.jpg"
+        placeholder_path = "assets/placeholder.jpg"  # Ensure this exists
         if not os.path.exists(placeholder_path):
             raise FileNotFoundError(f"Placeholder image not found at {placeholder_path}")
         images_and_locs = [(placeholder_path, "No images found")]
 
+    # Prepare prompt for the LLM
     img_desc = "\n".join(similar_images["documents"][0]) if images_and_locs else "No Images Are Provided"
     template = """
     Context:
@@ -270,8 +275,13 @@ def conversation(
     prompt = PromptTemplate(template=template, input_variables=["context", "question"])
     context = "\n\n".join(results)
 
+    # Generate response
     response = llm(prompt.format(context=context, question=msg, images=img_desc))
 
+    # Validate `images_and_locs` before returning
+    if not all(isinstance(item, tuple) and len(item) == 2 for item in images_and_locs):
+        raise ValueError("ret_images must be a list of (media, caption) tuples.")
+
     return history + [(msg, response)], results, images_and_locs