Update app.py

app.py

@@ -7,14 +7,14 @@ from rank_bm25 import BM25Okapi
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# ── 1. Auth & Models ──────────────────────────────────────────
+#  1. Auth & Models 
 HF_TOKEN = os.getenv("HF_TOKEN")
 PRIMARY_MODEL = "google/gemma-4-31B-it"
 FALLBACK_MODEL = "google/gemma-2-2b-it"
 
 client = InferenceClient(token=HF_TOKEN)
 
-# ── 2. RAG Utilities ──────────────────────────────────────────
+# 2. RAG Utilities 
 @dataclass(frozen=True)
 class Chunk:
     chunk_id: str
@@ -60,7 +60,7 @@ def grounding_score(answer: str, chunks: List[Chunk]) -> float:
     overlap = len(words & ref_words) / max(len(words), 1)
     return (len(cited) / len(sentences) * 0.7) + (overlap * 0.3)
 
-# ── 3. Global State ───────────────────────────────────────────
+#  3. Global State 
 CURRENT_INDEX = None
 CURRENT_CHUNKS: List[Chunk] = []
 
@@ -79,24 +79,24 @@ def process_uploads(files):
         p = Path(fp)
         print(f"[DEBUG] Processing: {p} (exists={p.exists()}, size={p.stat().st_size if p.exists() else 'N/A'})")
         if p.suffix.lower() != ".txt":
-            return f"⚠️ Only .txt files are supported. Unsupported file: **{p.name}**"
+            return f"Only .txt files are supported. Unsupported file: **{p.name}**"
 
         all_chunks.extend(load_text(p))
 
     if not all_chunks:
-        return f"⚠️ No text extracted from {len(files)} file(s). Check file content."
+        return f" No text extracted from {len(files)} file(s). Check file content."
 
     corpus = [_tokenize(c.text) for c in all_chunks]
     CURRENT_INDEX = BM25Okapi(corpus)
     CURRENT_CHUNKS = all_chunks
 
     preview = "\n".join(f"  • Chunk {i+1}: {c.text[:80]}…" for i, c in enumerate(all_chunks[:3]))
-    return f"✅ Indexed **{len(all_chunks)} chunks** from {len(files)} file(s).\n\n{preview}"
+    return f" Indexed **{len(all_chunks)} chunks** from {len(files)} file(s).\n\n{preview}"
 
 # ── 4. Agentic Pipeline with Reasoning Trace ─────────────────
 def agentic_answer(question: str):
     if not HF_TOKEN:
-        return "⚠️ Missing HF_TOKEN! Add it in Space Settings → Secrets.", "", "", "", ""
+        return " Missing HF_TOKEN! Add it in Space Settings → Secrets.", "", "", "", ""
     if CURRENT_INDEX is None:
         return "Upload document(s) first!", "", "", "", ""
 
@@ -148,7 +148,7 @@ def agentic_answer(question: str):
             continue
 
     if not answer_text:
-        return "🚨 Both models unavailable. Check your HF_TOKEN and model access.", "", "", "", ""
+        return " Both models unavailable. Check your HF_TOKEN and model access.", "", "", "", ""
 
     # Step 3: Split Reasoning Trace from Final Answer
     if "<reasoning_trace>" in answer_text:
@@ -156,7 +156,7 @@ def agentic_answer(question: str):
         trace = parts[0].replace("<reasoning_trace>", "").strip()
         final_ans = parts[1].strip() if len(parts) > 1 else "Analysis complete."
     else:
-        trace = "ℹ️ *Standard inference performed — no explicit trace returned by model.*"
+        trace = " *Standard inference performed — no explicit trace returned by model.*"
         final_ans = answer_text
 
     # Step 4: Compute Trust & Safety Badges
@@ -165,7 +165,7 @@ def agentic_answer(question: str):
 
     trust_cls = "high-trust" if score > 0.5 else "low-trust"
     safety_cls = "safety-refusal" if is_refusal else "safety-pass"
-    safety_label = "⚠️ Safety Advisory" if is_refusal else "✅ Safety: PASSED"
+    safety_label = " Safety Advisory" if is_refusal else " Safety: PASSED"
 
     trust_html = f'<div class="badge {trust_cls}">Trust: {score:.2f} ({model_used})</div>'
     safety_html = f'<div class="badge {safety_cls}">{safety_label}</div>'
@@ -173,7 +173,7 @@ def agentic_answer(question: str):
 
     return final_ans, trace, sources_md, trust_html, safety_html
 
-# ── 5. UI ─────────────────────────────────────────────────────
+#  5. UI 
 CUSTOM_CSS = """
 :root { --p: #2c5282; --bg: #0f1117; --accent: #805ad5; }
 .gradio-container { background: var(--bg) !important; color: #e2e8f0 !important; font-family: system-ui, sans-serif; }
@@ -209,7 +209,7 @@ button.primary { background: linear-gradient(135deg, #2c5282, #805ad5) !importan
 
 with gr.Blocks(css=CUSTOM_CSS, title="Medical RAG Studio", theme=gr.themes.Soft()) as demo:
     with gr.Column(elem_id="header"):
-        gr.Markdown("# 🔬 Medical RAG Studio\n### High-Trust Agentic Intelligence for Health Discovery")
+        gr.Markdown("#  Medical RAG Studio\n### High-Trust Agentic Intelligence for Health Discovery")
 
     with gr.Row():
         with gr.Column(scale=2):
@@ -220,7 +220,7 @@ with gr.Blocks(css=CUSTOM_CSS, title="Medical RAG Studio", theme=gr.themes.Soft(
             btn = gr.Button("Analyze with Gemma", variant="primary")
 
             # Demo Questions for Judges
-            gr.Markdown("### 🎯 Try These Demo Queries")
+            gr.Markdown("###  Try These Demo Queries")
             gr.Examples(
                 examples=[
                     ["What are the symptoms of a common cold according to the documents?"],
@@ -241,7 +241,7 @@ with gr.Blocks(css=CUSTOM_CSS, title="Medical RAG Studio", theme=gr.themes.Soft(
             ans = gr.Markdown(elem_id="ans")
 
             # Reasoning Trace Accordion — Explainable AI for judges
-            with gr.Accordion("🔍 View Gemma's Reasoning Trace", open=False, elem_id="trace-accordion"):
+            with gr.Accordion(" View Gemma's Reasoning Trace", open=False, elem_id="trace-accordion"):
                 trace_output = gr.Markdown(
                     value="*Reasoning trace will appear here after analysis...*"
                 )