Update app.py

app.py

@@ -13,10 +13,9 @@ model = AutoModelForCausalLM.from_pretrained(
     device_map="cpu"
 )
 
-# ----- Trend detection (math-based, NOT LLM) -----
+# ----- Trend detection -----
 def detect_trend(values):
     diffs = np.diff(values)
-
     if all(d > 0 for d in diffs):
         return "INCREASING"
     elif all(d < 0 for d in diffs):
@@ -24,19 +23,12 @@ def detect_trend(values):
     else:
         return "MIXED"
 
-# ----- Simple anomaly detection -----
+# ----- Anomaly detection -----
 def detect_anomaly(values):
     mean = np.mean(values)
     std = np.std(values)
-
-    anomalies = []
-    for i, v in enumerate(values):
-        if abs(v - mean) > 2 * std:
-            anomalies.append((i, v))
-
-    if len(anomalies) == 0:
-        return "No anomalies detected"
-    return str(anomalies)
+    anomalies = [(i, v) for i, v in enumerate(values) if abs(v - mean) > 2 * std]
+    return "No anomalies detected" if len(anomalies) == 0 else str(anomalies)
 
 # ----- LLM explanation -----
 def explanation(entity, values, trend):
@@ -45,36 +37,36 @@ You are a KPI analysis expert.
 The entity is: {entity}
 The values are: {values}
 The detected trend is: {trend}
-
 Explain in simple words why the trend is {trend}.
 """
     inputs = tokenizer(prompt, return_tensors="pt")
     outputs = model.generate(**inputs, max_new_tokens=150)
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# ----- Main function -----
-def analyze(entity, value_string):
+# ----- Wrapper with loading message -----
+def analyze_with_spinner(entity, value_string):
+    yield "⏳ **Analyzing... please wait...**"
     try:
         values = [float(x.strip()) for x in value_string.split(",")]
     except:
-        return "❌ Error: Please enter numbers separated by commas"
+        yield "❌ Error: Please enter numbers separated by commas"
+        return
 
     trend = detect_trend(values)
     anomaly = detect_anomaly(values)
     exp = explanation(entity, values, trend)
 
-    return f"""
-📌 **Entity:** {entity}
-📉 **Trend:** {trend}
-⚠️ **Anomalies:** {anomaly}
-
+    yield f"""
+📌 **Entity:** {entity}  
+📉 **Trend:** {trend}  
+⚠️ **Anomalies:** {anomaly}  
 🧠 **Explanation:**  
 {exp}
 """
 
 # ----- UI -----
 with gr.Blocks() as demo:
-    gr.Markdown("# 📈 KPI Trend + Anomaly Analyzer (CPU-Friendly)")
+    gr.Markdown("# 📈 KPI Analyzer")
 
     entity = gr.Textbox(label="KPI Name (example: volte_rate)")
     values = gr.Textbox(label="Values (comma separated, example: 10,11,13,14,15)")
@@ -82,6 +74,10 @@ with gr.Blocks() as demo:
 
     run_btn = gr.Button("Analyze")
 
-    run_btn.click(fn=analyze, inputs=[entity, values], outputs=output)
+    run_btn.click(
+        fn=analyze_with_spinner,
+        inputs=[entity, values],
+        outputs=output,
+    )
 
 demo.launch()