Update gradio_new_rag_app.py

gradio_new_rag_app.py

@@ -1,4 +1,12 @@
 #!/usr/bin/env python3
+"""
+Gradio UI for the structured RAG AI Search.
+
+NEW FEATURE:
+- After Region + Sub-Zone selection, show 1-4 "Common concerns" (internet -> fallback DB).
+- Clicking a concern auto-fills the Issue textbox.
+"""
+
 import json
 import os
 import pandas as pd
@@ -6,18 +14,16 @@ import gradio as gr
 
 from rag_treatment_app import RAGTreatmentSearchApp
 
+
 APP_TITLE = "Aesthetic AI Search (Structured RAG - Region → Sub-Zone → Issue → Type)"
 
+
 def format_answer_markdown(out: dict) -> str:
     if not isinstance(out, dict):
         return "No output."
     answer_md = (out.get("answer_md") or "").strip()
-    sources = out.get("sources") or []
-    md = [answer_md if answer_md else "No answer generated."]
-    if sources:
-        md.append("\n---\n## Sources")
-        md.extend([f"- {u}" for u in sources[:20]])
-    return "\n".join(md).strip()
+    return answer_md if answer_md else "No answer generated."
+
 
 def build_debug_table(data: dict):
     rows = []
@@ -32,6 +38,7 @@ def build_debug_table(data: dict):
         })
     return pd.DataFrame(rows)
 
+
 def make_app():
     rag = RAGTreatmentSearchApp(
         excel_path=os.getenv("DB_XLSX", "database.xlsx"),
@@ -45,6 +52,14 @@ def make_app():
             return []
         return rag.get_sub_zones(region)
 
+    def concerns_for(region, sub_zone):
+        if not region or not sub_zone:
+            return []
+        try:
+            return rag.get_common_concerns(region, sub_zone, n=4)
+        except Exception:
+            return []
+
     def run_search(region, sub_zone, issue, pref, retrieval_k, final_k, show_debug):
         out = rag.answer(
             region=region,
@@ -57,22 +72,37 @@ def make_app():
         md = format_answer_markdown(out)
         dbg_df = build_debug_table(out) if show_debug else pd.DataFrame()
         raw = json.dumps(out, ensure_ascii=False, indent=2)
-        return md, dbg_df, raw, gr.update(visible=show_debug), gr.update(open=False)
+        return md, dbg_df, raw
 
     with gr.Blocks(title=APP_TITLE) as demo:
         gr.Markdown(f"# {APP_TITLE}")
 
         with gr.Row():
-            region = gr.Dropdown(choices=regions, label="Region (Body part)", value=regions[0] if regions else None)
-            sub_zone = gr.Dropdown(choices=subzones_for_region(regions[0] if regions else ""), label="Sub-Zone", interactive=True)
-
-        def _update_subzones(r):
-            return gr.Dropdown(choices=subzones_for_region(r), value=None)
-
-        region.change(_update_subzones, inputs=[region], outputs=[sub_zone])
+            region = gr.Dropdown(
+                choices=regions,
+                label="Region (Body part)",
+                value=regions[0] if regions else None
+            )
+            sub_zone = gr.Dropdown(
+                choices=subzones_for_region(regions[0] if regions else ""),
+                label="Sub-Zone",
+                interactive=True
+            )
+
+        # NEW: common concerns selector
+        common_concerns = gr.Radio(
+            choices=[],
+            value=None,
+            label="Common concerns (optional) — click one to auto-fill Issue",
+            interactive=True
+        )
 
         issue = gr.Textbox(lines=3, label="Describe your issue/problem (free text, multilingual supported)")
-        pref = gr.Radio(["Surgical Treatment", "Non-surgical Treatment", "Both"], value="Both", label="Treatment preference")
+        pref = gr.Radio(
+            ["Surgical Treatment", "Non-surgical Treatment", "Both"],
+            value="Both",
+            label="Treatment preference"
+        )
 
         with gr.Row():
             retrieval_k = gr.Slider(5, 30, value=12, step=1, label="Retrieval candidates (semantic top-K)")
@@ -82,15 +112,60 @@ def make_app():
         run_btn = gr.Button("Run AI Search")
 
         md_out = gr.Markdown()
+        dbg_out = gr.Dataframe(label="Debug: Semantic candidates")
+        raw_json = gr.Code(label="Raw JSON")
 
-        dbg_out = gr.Dataframe(label="Debug: Semantic candidates", visible=False)
-        with gr.Accordion("Raw JSON (advanced)", open=False) as raw_acc:
-            raw_json = gr.Code(label="Raw JSON")
+        # --- events ---
+
+        def _update_subzones_and_concerns(r):
+            subz = subzones_for_region(r)
+            # reset subzone selection, and clear concerns until subzone is picked
+            return gr.Dropdown(choices=subz, value=None), gr.Radio(choices=[], value=None)
+
+        region.change(
+            _update_subzones_and_concerns,
+            inputs=[region],
+            outputs=[sub_zone, common_concerns]
+        )
+
+        def _update_concerns(r, sz):
+            opts = concerns_for(r, sz)
+            return gr.Radio(choices=opts, value=None)
+
+        sub_zone.change(
+            _update_concerns,
+            inputs=[region, sub_zone],
+            outputs=[common_concerns]
+        )
+
+        # click concern -> fill issue text
+        def _fill_issue_from_concern(c):
+            if not c:
+                return gr.update()
+            return c
+
+        common_concerns.change(
+            _fill_issue_from_concern,
+            inputs=[common_concerns],
+            outputs=[issue]
+        )
 
         run_btn.click(
             run_search,
             inputs=[region, sub_zone, issue, pref, retrieval_k, final_k, show_debug],
-            outputs=[md_out, dbg_out, raw_json, dbg_out, raw_acc],
+            outputs=[md_out, dbg_out, raw_json],
+        )
+
+        gr.Markdown(
+            """
+### Note
+If internet access is blocked/rate-limited on Hugging Face, the app automatically falls back to your DB to suggest common concerns.
+"""
         )
 
     return demo
+
+
+if __name__ == "__main__":
+    demo = make_app()
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)