Update app.py

app.py

@@ -27,9 +27,9 @@ exadata_specs = {
 
 # --- Supported LLM Models ---
 supported_llms = {
-    "gpt-3.5-turbo": "Fastest / lowest cost (basic analysis), Standard AWR Healthcheck",
-    "gpt-4-turbo": "Balanced (recommended default), Production Performance Analysis",
-    "gpt-4o": "Deep + technical (best), Deep Dive, Exadata, RAC Stability, Risk Audit",
+    "gpt-3.5-turbo": "Fastest / Lowest Cost - General AWR Healthcheck",
+    "gpt-4-turbo": "Balanced - Production Performance Analysis",
+    "gpt-4o": "Deepest Analysis - Exadata, RAC, Smart Scan, Critical Issues",
 }
 
 # --- Utils ---
@@ -89,7 +89,7 @@ Compare observed vs theoretical. Recommend actions to close the performance gap.
         return response.choices[0].message.content.strip()
 
 class HealthAgent:
-    def check_health(self, content):
+    def check_health(self, content, llm_model):
         cleaned_content = clean_awr_content(content)
         if len(cleaned_content) > 128000:
             cleaned_content = cleaned_content[:128000] + "\n\n[TRUNCATED]..."
@@ -107,24 +107,12 @@ You MUST:
 - Always confirm at the end if no CRITICAL or WARNING issues were found.
 
 Special Attention Areas:
-- Flash Cache or Flash Disk Failures (including confined, offline, failed or dropped disks)
-- I/O Subsystem stalls or device errors
-- ASM Disk or Grid Disk issues
-- Smart Scan failures or skipped Smart Scans
-- Redo Log issues (e.g. log file sync slow)
-- RAC Interconnect problems (e.g. gc buffer busy waits, ORA-12170)
-
-You are NOT allowed to generalize or omit any issue, no matter how small.
-
-Specifically analyze Exadata Alerts Summary and Exadata Alerts Detail
-
-Example Output:
-🚨 CRITICAL
-- [issue]
-⚠️ WARNING
-- [issue]
-✅ INFO
-- No other warnings or critical issues detected.
+- Flash Cache or Flash Disk Failures
+- I/O Subsystem stalls or errors
+- ASM/Grid Disk issues
+- Smart Scan failures
+- Redo Log issues
+- RAC Interconnect issues
 
 AWR CONTENT:
 {cleaned_content}
@@ -143,15 +131,12 @@ AWR CONTENT:
 class RaterAgent:
     def rate(self, content):
         prompt = f"Rate the following analysis from 1-5 stars and explain:\n\n{content}"
-        
         response = openrouter.chat.completions.create(
-            model="mistralai/Mixtral-8x7B-Instruct",  # FORCE to use Mixtral via OpenRouter
+            model="mistralai/Mixtral-8x7B-Instruct",
             messages=[{"role": "user", "content": prompt}]
         )
-        
         return response.choices[0].message.content.strip()
 
-
 # === Main Process ===
 def process_awr(awr_text, threshold, performance_test_mode, exadata_model, rack_size, llm_model):
     analyzer = CriticalAnalyzerAgent()
@@ -174,7 +159,7 @@ def process_awr(awr_text, threshold, performance_test_mode, exadata_model, rack_
 
     if stars < threshold:
         analysis_retry = analyzer.analyze(awr_text, performance_test_mode, exadata_model, rack_size, llm_model)
-        rating_text_retry = rater.rate(analysis_retry, llm_model)
+        rating_text_retry = rater.rate(analysis_retry)
         retry_status = "✅ Retry Occurred"
         analysis = analysis_retry
         rating_text = rating_text_retry