Update app.py

app.py

@@ -1,162 +1,93 @@
-
-"""
-Document Q&A with Manual RAG
-Hugging Face Spaces - CPU Compatible Version
-"""
+"""Document Q&A with Manual RAG - Hugging Face CPU Basic."""
 
 import os
 import traceback
 
-# Force CPU execution before loading ML libraries
+# Set this before importing the ML libraries used by rag_core.
 os.environ["CUDA_VISIBLE_DEVICES"] = ""
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
+os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
 
 import gradio as gr
 
-from rag_core import (
-    load_retriever,
-    answer_question,
-    display_evidence,
-)
+from rag_core import load_retriever, answer_question, display_evidence
 
 
 def respond(question, retrieval_only):
-    """Retrieve evidence and optionally generate an answer."""
-
-    if not question or not question.strip():
-        return (
-            "Please enter a question.",
-            "",
-            "Input error"
-        )
+    """Retrieve evidence and optionally generate a grounded answer."""
+    if not isinstance(question, str) or not question.strip():
+        return "Please enter a question.", "", "Input error"
 
     try:
         result = answer_question(
-            question.strip(),
-            retrieval_only=bool(retrieval_only)
-        )
-
-        return (
-            result.get("answer", "No answer generated."),
-            display_evidence(result),
-            result.get("status", "Completed")
+            question.strip(), retrieval_only=bool(retrieval_only)
         )
+        return result["answer"], display_evidence(result), result["status"]
 
     except (ValueError, RuntimeError) as error:
         traceback.print_exc()
+        return str(error), "", "Input or setup error"
 
-        return (
-            f"Error: {str(error)}",
-            "",
-            "Input or setup error"
-        )
-
-    except Exception as error:
+    except Exception:
         traceback.print_exc()
-
         return (
-            "Dataset/model access or generation failed. "
-            f"Details: {str(error)}",
+            "Dataset/model access or generation failed. Check the Space logs.",
             "",
-            "Model error"
+            "Model error",
         )
 
 
 def make_demo():
-    """Create the Gradio interface."""
-
     return gr.Interface(
         fn=respond,
-
         inputs=[
             gr.Textbox(
                 label="Question",
                 placeholder="Which company produces the iPod?",
-                lines=2
+                lines=2,
             ),
-
             gr.Checkbox(
                 label="Retrieval only (skip answer generation)",
-                value=False
-            )
+                value=False,
+            ),
         ],
-
         outputs=[
-            gr.Textbox(
-                label="Answer - verify against evidence",
-                lines=5
-            ),
-
-            gr.Textbox(
-                label="Source passages and similarity scores",
-                lines=15
-            ),
-
-            gr.Textbox(
-                label="Run status",
-                lines=2
-            )
+            gr.Textbox(label="Answer - verify against the evidence", lines=5),
+            gr.Textbox(label="Source passages and similarity scores", lines=15),
+            gr.Textbox(label="Run status"),
         ],
-
         title="Document Q&A with Manual RAG",
-
         description=(
-            "Ask questions about 30 SQuAD/Wikipedia passages "
-            "loaded from Hugging Face. "
-            "The application retrieves relevant evidence "
-            "and optionally generates an answer. "
-            "Always verify answers against the sources."
+            "Ask questions about 30 SQuAD/Wikipedia passages loaded from "
+            "Hugging Face. The app retrieves evidence and optionally generates "
+            "an answer. Always verify answers against the sources."
         ),
-
         examples=[
             ["Which company produces the iPod?", False],
             ["At what age did Frédéric move to Paris?", False],
-            ["What is Beyoncé's private mobile phone number?", False]
+            ["What is Beyoncé's private mobile phone number?", False],
         ],
-
         cache_examples=False,
-
-        flagging_mode="never"
     )
 
 
 def main():
-    """Initialize retrieval system and launch the application."""
-
-    print("=" * 50)
-    print("Initializing Document Q&A with Manual RAG")
-    print("Execution mode: CPU")
-    print("=" * 50)
-
+    print("Initializing Document Q&A with Manual RAG (CPU mode)...")
     try:
-        print("Loading retriever...")
-
-        info = load_retriever()
-
-        print(f"Retriever initialized successfully: {info}")
-
+        shape = load_retriever()
+        print("Retriever initialized:", shape)
     except Exception:
         print("Failed to initialize retriever.")
         traceback.print_exc()
         raise
 
-    print("Creating Gradio interface...")
-
     demo = make_demo()
-
-    demo.queue(
-        default_concurrency_limit=1,
-        max_size=20
-    )
-
-    print("Starting Gradio application...")
-
+    demo.queue(default_concurrency_limit=1, max_size=20)
     demo.launch(
         server_name="0.0.0.0",
         server_port=int(os.environ.get("PORT", "7860")),
         share=False,
         show_error=True,
-        ssr_mode=False
+        ssr_mode=False,
     )