Upload app.py

app.py

@@ -9,12 +9,10 @@ from langchain_huggingface import HuggingFaceEmbeddings
 repo_id = "robertolofaro/articles-model"
 
 BACKENDS = {
-    "Chroma - RAG": "Chroma",
     "FAISS - RAG (HNSW)": "FAISS",
     "Qdrant - RAG": "Qdrant"
 }
 
-CHROMA_PATH = "chroma_db"
 FAISS_PATH = "faiss_index_hnsw"
 QDRANT_PATH = "qdrant_db"
 QDRANT_COLLECTION = "articles"
@@ -59,10 +57,7 @@ def get_vectorstore(backend_name: str):
     try:
         embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-small-en-v1.5", encode_kwargs={'normalize_embeddings': True})
         
-        if backend_name == "Chroma":
-            from langchain_community.vectorstores import Chroma
-            vs = Chroma(persist_directory=CHROMA_PATH, embedding_function=embeddings)
-        elif backend_name == "FAISS":
+        if backend_name == "FAISS":
             from langchain_community.vectorstores import FAISS
             vs = FAISS.load_local(FAISS_PATH, embeddings, allow_dangerous_deserialization=True)
         elif backend_name == "Qdrant":
@@ -140,7 +135,7 @@ def generate_response(message, history, rag_mode, article_filter, max_tokens, te
 # ====================== GRADIO INTERFACE ======================
 with gr.Blocks(title="Article Q&A model") as demo:
     gr.Markdown("# sourcing 350+ articles on change")
-    gr.Markdown("Qwen3.5-4B DoRA fine-tuned on 350+ articles on change from robertolofaro.com - experimental on CPU-only, to test embedding methods (takes few minutes, no selection for the category yet)")
+    gr.Markdown("Qwen3.5-4B DoRA fine-tuned on 350+ articles on change from robertolofaro.com - experimental on CPU-only, to test embedding methods (takes few minutes, no selection for the category yet) - updated as of 2026-05-05")
 
     with gr.Row():
         rag_mode = gr.Radio(