Refactor conversation function to remove commented code and improve placeholder image handling

app.py

@@ -204,7 +204,6 @@ def conversation(
     hf_token,
     model_path,
 ):
-    # Initialize the LLM based on inputs
     if hf_token.strip() != "" and model_path.strip() != "":
         llm = HuggingFaceEndpoint(
             repo_id=model_path,
@@ -220,7 +219,6 @@ def conversation(
             huggingfacehub_api_token=os.getenv("P_HF_TOKEN", "None"),
         )
 
-    # Retrieve collections from vector database
     text_collection = vectordb_client.get_collection(
         "text_db", embedding_function=sentence_transformer_ef
     )
@@ -228,19 +226,16 @@ def conversation(
         "image_db", embedding_function=sentence_transformer_ef
     )
 
-    # Query text context
     results = text_collection.query(
         query_texts=[msg], include=["documents"], n_results=num_context
     )["documents"][0]
 
-    # Query image context
     similar_images = image_collection.query(
         query_texts=[msg],
         include=["metadatas", "distances", "documents"],
         n_results=img_context,
     )
 
-    # Initialize image links and descriptions
     img_links = similar_images["metadatas"][0] if similar_images["metadatas"] else []
     images_and_locs = []
 
@@ -252,11 +247,12 @@ def conversation(
         except Exception as e:
             print(f"Error decoding image: {e}")
 
-    # Handle case where no images are found
     if not images_and_locs:
-        images_and_locs = [("path/to/placeholder/image.jpg", "No images found")]
+        placeholder_path = "assets/placeholder.jpg"
+        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:
@@ -274,14 +270,13 @@ 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))
 
-    # Return updated history, text results, and image locations
     return history + [(msg, response)], results, images_and_locs
 
 
 
+
 def check_validity_and_llm(session_states):
     if session_states.get("processed", False) == True:
         return gr.Tabs(selected=2)