Update app.py

app.py

@@ -3,7 +3,7 @@ import pandas as pd
 from transformers import pipeline
 
 # ------------------------------------------------
-# Load Qwen 3B
+# Load Qwen 3B (instruction-following, concise)
 # ------------------------------------------------
 generator = pipeline(
     "text-generation",
@@ -13,7 +13,7 @@ generator = pipeline(
 )
 
 # ------------------------------------------------
-# Quantization helpers (FACTS only)
+# Fact quantization (GENERIC)
 # ------------------------------------------------
 def magnitude_bucket(x):
     if x < 0.05:
@@ -43,68 +43,77 @@ def analyze_kpi(csv_file, top_n):
     df["diff"] = df[curr_date] - df[prev_date]
     df["abs_diff"] = df["diff"].abs()
 
-    ranked = df.sort_values("abs_diff", ascending=False).head(top_n)
+    ranked = df.sort_values("abs_diff", ascending=False)
+
+    top_kpis = ranked.head(3)
+    other_kpis = ranked.iloc[3:]
 
     # -------------------------------
-    # Primary KPI facts
+    # Top 3 KPI FACT BLOCKS
     # -------------------------------
-    primary = ranked.iloc[0]
-
-    primary_facts = {
-        "PRIMARY_KPI": primary["Kpi"],
-        "PRIMARY_DIRECTION": direction_bucket(primary["diff"]),
-        "PRIMARY_CHANGE": round(primary["abs_diff"], 2),
-        "PRIMARY_MAGNITUDE": magnitude_bucket(primary["abs_diff"]),
-        "PRIMARY_UNIT": "percentage points" if "%" in primary["Kpi"] else "units"
-    }
+    top_facts = []
+    for _, row in top_kpis.iterrows():
+        top_facts.append({
+            "KPI": row["Kpi"],
+            "DIRECTION": direction_bucket(row["diff"]),
+            "CHANGE_VALUE": round(row["abs_diff"], 2),
+            "MAGNITUDE": magnitude_bucket(row["abs_diff"]),
+            "UNIT": "percentage points" if "%" in row["Kpi"] else "units"
+        })
 
     # -------------------------------
-    # Secondary KPI facts
+    # Remaining KPI AGGREGATED FACTS
     # -------------------------------
-    secondary = ranked.iloc[1:]
-
-    secondary_facts = {
-        "SECONDARY_COUNT": len(secondary),
-        "SECONDARY_AVG_CHANGE": round(secondary["abs_diff"].mean(), 2),
-        "SECONDARY_MAGNITUDE": magnitude_bucket(secondary["abs_diff"].mean()),
-        "SECONDARY_DIRECTION_BALANCE": (
-            "mostly_increase" if (secondary["diff"] > 0).mean() > 0.7
-            else "mostly_decrease" if (secondary["diff"] > 0).mean() < 0.3
-            else "mixed"
-        )
-    }
+    if len(other_kpis) > 0:
+        other_facts = {
+            "KPI_COUNT": len(other_kpis),
+            "AVG_CHANGE": round(other_kpis["abs_diff"].mean(), 2),
+            "MAGNITUDE": magnitude_bucket(other_kpis["abs_diff"].mean()),
+            "DIRECTION_BALANCE": (
+                "mostly_increase" if (other_kpis["diff"] > 0).mean() > 0.7
+                else "mostly_decrease" if (other_kpis["diff"] > 0).mean() < 0.3
+                else "mixed"
+            )
+        }
+    else:
+        other_facts = None
 
     # -------------------------------
-    # Model input = FACT BLOCK
+    # Model input = PURE FACTS
     # -------------------------------
     model_input = (
-        "Generate a short operational summary from the following facts.\n\n"
-        f"{primary_facts}\n"
-        f"{secondary_facts}"
+        "Write a short insight-style summary using only the facts below.\n\n"
+        "TOP_KPI_FACTS:\n"
+        f"{top_facts}\n\n"
+        "OTHER_KPI_FACTS:\n"
+        f"{other_facts}"
     )
 
     output = generator(
         model_input,
-        max_new_tokens=80,
+        max_new_tokens=120,
         do_sample=False
     )[0]["generated_text"]
 
-    return ranked[["Kpi", "abs_diff"]], output
+    return ranked.head(top_n)[["Kpi", "abs_diff"]], output
 
 # ------------------------------------------------
-# UI
+# UI (HF Space)
 # ------------------------------------------------
-with gr.Blocks(title="KPI Summary Generator") as demo:
-    gr.Markdown("## KPI Change Summary")
-    gr.Markdown("Upload CSV. Summary is generated strictly from data-derived facts.")
+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."
+    )
 
     csv_input = gr.File(file_types=[".csv"])
-    top_n_input = gr.Slider(3, 5, value=3, step=1)
+    top_n_input = gr.Slider(3, 6, value=5, step=1, label="KPIs to Display")
 
-    btn = gr.Button("Generate")
+    btn = gr.Button("Generate Insight")
 
-    table = gr.Dataframe()
-    summary = gr.Textbox(lines=3)
+    table = gr.Dataframe(label="Top KPI Changes")
+    summary = gr.Textbox(label="Insight Summary", lines=4)
 
     btn.click(
         analyze_kpi,