Update app.py

app.py

@@ -1,44 +1,98 @@
 """
-Step 3: App with an admin button to build the TXT index.
+Step 4: Minimal Q→A using the FAISS index.
 
-What changed (Objective):
-- We import build_index() from indexer.py
-- We add a "Build/Refresh Index" button
-- We show the status message in the UI
+What changed vs Step 3:
+- We import RAGSearcher, summarize_hits, format_sources from rag_search.py
+- We add a question input + "Ask" button and display:
+    - an extractive-style answer (placeholder)
+    - a "Sources" section showing which files informed the answer
+- We lazy-load the searcher when the user first asks a question
 """
 
 import gradio as gr
-from indexer import build_index  # (Objective) Our Step 3 function
 
+from indexer import build_index
+from rag_search import RAGSearcher, summarize_hits, format_sources
+
+# We'll instantiate the searcher lazily so the app can boot even if the index isn't built yet.
+_searcher = None
+
+def ensure_searcher():
+    """
+    Create the global RAGSearcher if not present.
+    If the index doesn't exist yet, raise a clear exception to show in the UI.
+    """
+    global _searcher
+    if _searcher is None:
+        _searcher = RAGSearcher()
+    return _searcher
 
 def on_build_index():
     """
-    Run the indexer and return its status message.
-    (Objective) Called when the user clicks the button.
+    Build/refresh the FAISS index from TXT files.
     """
     try:
         msg = build_index()
+        # Reset searcher so a new one is created against the fresh index
+        global _searcher
+        _searcher = None
     except Exception as e:
         msg = f"Error while building index: {e}"
     return msg
 
-with gr.Blocks(title="Evo Government Copilot (MU) - Step 3") as demo:
+def on_ask(question: str, top_k: int, history: list):
+    """
+    Handle a user question:
+    - Ensure the index is available
+    - Retrieve top-k chunks
+    - Produce a concise extractive answer + sources (Step 5 will swap in Evo)
+    - Append to the chat history
+    """
+    if not question or len(question.strip()) < 3:
+        return history, "Please enter a question (at least 3 characters).", "Sources: (none)"
+
+    try:
+        searcher = ensure_searcher()
+        hits = searcher.search(question, k=int(top_k))
+        answer = summarize_hits(hits)
+        sources = format_sources(hits)
+    except Exception as e:
+        # Most common cause: index not built yet
+        answer = f"Error: {e}\n\nTip: Click 'Build/Refresh Index' first."
+        sources = "Sources: (none)"
+
+    # Update the chat UI (user message + assistant reply)
+    history = history + [(question, answer)]
+    return history, answer, sources
+
+
+with gr.Blocks(title="Evo Government Copilot (MU) - Step 4") as demo:
     gr.Markdown(
         """
-        # 🇲🇺 Evo Government Copilot (Mauritius) — Step 3
-        ✅ Space is running.  
-        **Now: Build the local semantic index (TXT only).**
-        
-        - Put *.txt files under `data/seed/` (Step 2 did this with placeholders).  
-        - Click **Build/Refresh Index** to create the FAISS index.  
-        - We add retrieval and answers in the next step.
+        # 🇲🇺 Evo Government Copilot (Mauritius) — Step 4
+        **Now live:** Ask a question → we retrieve top chunks → show a concise answer + sources.
+
+        ✅ TXT indexing from Step 3  
+        ✅ Retrieval + Q→A (extractive)  
+        ⏭️ Next: Evo synthesis + languages (EN/FR/Kreol), links & forms
         """
     )
 
     with gr.Row():
         build_btn = gr.Button("🔧 Build/Refresh Index (TXT)")
-    status = gr.Markdown("Status: _idle_")
-
+        status = gr.Markdown("Status: _idle_")
     build_btn.click(fn=on_build_index, outputs=status)
 
+    gr.Markdown("### Ask a question")
+    with gr.Row():
+        q = gr.Textbox(placeholder="e.g., How do I renew my Mauritius passport?", label="Your question")
+        k = gr.Slider(3, 12, step=1, value=6, label="Context depth (top-k)")
+        ask_btn = gr.Button("Ask")
+    chat = gr.Chatbot(label="Assistant", height=360)
+    answer_md = gr.Markdown()
+    sources_md = gr.Markdown()
+
+    ask_btn.click(fn=on_ask, inputs=[q, k, chat], outputs=[chat, answer_md, sources_md])
+    q.submit(fn=on_ask, inputs=[q, k, chat], outputs=[chat, answer_md, sources_md])
+
 demo.launch()