Update app.py

app.py

@@ -13,7 +13,7 @@ generator = pipeline(
 )
 
 # ------------------------------------------------
-# Fact quantization (GENERIC)
+# Generic fact quantization (NO domain logic)
 # ------------------------------------------------
 def magnitude_bucket(x):
     if x < 0.05:
@@ -79,7 +79,8 @@ def analyze_kpi(csv_file, top_n):
         other_facts = None
 
     # -------------------------------
-    # Model input = PURE FACTS
+    # Model input = FACT BLOCK ONLY
+    # (no narrative text embedded)
     # -------------------------------
     model_input = (
         "Write a short insight-style summary using only the facts below.\n\n"
@@ -89,26 +90,38 @@ def analyze_kpi(csv_file, top_n):
         f"{other_facts}"
     )
 
+    # -------------------------------
+    # Controlled variation generation
+    # -------------------------------
     output = generator(
         model_input,
         max_new_tokens=120,
-        do_sample=False
+        do_sample=True,
+        temperature=0.6,
+        top_p=0.85,
+        repetition_penalty=1.1
     )[0]["generated_text"]
 
     return ranked.head(top_n)[["Kpi", "abs_diff"]], output
 
 # ------------------------------------------------
-# UI (HF Space)
+# Gradio UI (Hugging Face Space)
 # ------------------------------------------------
 with gr.Blocks(title="Network Insight Summary") as demo:
     gr.Markdown("## 📊 Network Insight Summary")
     gr.Markdown(
         "Upload KPI CSV to generate a concise, insight-style summary "
-        "focused on the most significant KPI changes."
+        "highlighting the most significant KPI changes."
     )
 
     csv_input = gr.File(file_types=[".csv"])
-    top_n_input = gr.Slider(3, 6, value=5, step=1, label="KPIs to Display")
+    top_n_input = gr.Slider(
+        minimum=3,
+        maximum=6,
+        value=5,
+        step=1,
+        label="KPIs to Display"
+    )
 
     btn = gr.Button("Generate Insight")