Update app.py

app.py

@@ -6,15 +6,10 @@ from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
 from llama_index.llms.openai import OpenAI
 from llama_index.embeddings.openai import OpenAIEmbedding
 
-
-# ======================
-# Config (safe defaults)
-# ======================
 MODEL = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
 EMBED_MODEL = os.getenv("OPENAI_EMBED_MODEL", "text-embedding-3-small")
 TOP_K = int(os.getenv("TOP_K", "3"))
 
-# Your knowledge base file in the Space repo
 DOC_PATH = Path(os.getenv("DOC_PATH", "challenge_context.txt"))
 
 SYSTEM_GUARDRAILS = (
@@ -23,39 +18,30 @@ SYSTEM_GUARDRAILS = (
     "Then ask the user to add the missing official details to challenge_context.txt."
 )
 
-
-# ======================
-# Build index (cached)
-# ======================
 _INDEX = None
 _QUERY_ENGINE = None
 
 def build_index():
     global _INDEX, _QUERY_ENGINE
-
     if _QUERY_ENGINE is not None:
         return _QUERY_ENGINE
 
-    api_key = os.getenv("OPENAI_API_KEY")
-    if not api_key:
+    if not os.getenv("OPENAI_API_KEY"):
         raise RuntimeError(
             "OPENAI_API_KEY is missing. Add it in the Space Settings → Variables and secrets."
         )
 
     if not DOC_PATH.exists():
-        # Create a placeholder so the Space boots even if you forgot the file
         DOC_PATH.write_text(
             "Add the official Building AI Application Challenge content here.\n",
             encoding="utf-8",
         )
 
-    # LlamaIndex global settings
     Settings.llm = OpenAI(model=MODEL, temperature=0.2)
     Settings.embed_model = OpenAIEmbedding(model=EMBED_MODEL)
     Settings.chunk_size = 800
     Settings.chunk_overlap = 120
 
-    # Reader expects a directory
     data_dir = str(DOC_PATH.parent)
     docs = SimpleDirectoryReader(
         input_dir=data_dir,
@@ -63,7 +49,6 @@ def build_index():
         recursive=False
     ).load_data()
 
-    # Only index the target file
     docs = [d for d in docs if d.metadata.get("file_name") == DOC_PATH.name]
     if not docs:
         raise FileNotFoundError(f"Could not load {DOC_PATH.name}. Make sure it exists in the repo.")
@@ -72,7 +57,6 @@ def build_index():
     _QUERY_ENGINE = _INDEX.as_query_engine(similarity_top_k=TOP_K)
     return _QUERY_ENGINE
 
-
 def format_sources(resp, max_sources=3, max_chars=220):
     lines = []
     for i, sn in enumerate(getattr(resp, "source_nodes", [])[:max_sources], start=1):
@@ -83,10 +67,8 @@ def format_sources(resp, max_sources=3, max_chars=220):
         lines.append(f"{i}. {fn}{score_txt}: {snippet}...")
     return "\n".join(lines) if lines else "No sources returned."
 
-
 def chat(message, history):
     qe = build_index()
-
     prompt = (
         f"{SYSTEM_GUARDRAILS}\n\n"
         f"User question: {message}\n"
@@ -102,17 +84,23 @@ def chat(message, history):
     return answer
 
 
-demo = gr.ChatInterface(
-    fn=chat,
-    title="Challenge Copilot — RAG Q&A Bot",
-    description="Ask questions about the Building AI Application Challenge using challenge_context.txt (LlamaIndex + OpenAI).",
-    examples=[
-        "What will I build in this live session?",
-        "Who is this best for?",
-        "What are the prerequisites?"
-    ],
-    theme="soft"
-)
+# ---- UI ----
+try:
+    theme_obj = gr.themes.Soft()
+except Exception:
+    theme_obj = None  # compatibility fallback
+
+with gr.Blocks(theme=theme_obj) as demo:
+    gr.Markdown("# Challenge Copilot — RAG Q&A Bot")
+    gr.Markdown("Ask questions about the Building AI Application Challenge using challenge_context.txt (LlamaIndex + OpenAI).")
+    gr.ChatInterface(
+        fn=chat,
+        examples=[
+            "What will I build in this live session?",
+            "Who is this best for?",
+            "What are the prerequisites?"
+        ],
+    )
 
 if __name__ == "__main__":
     demo.launch()