Update app.py

app.py

@@ -86,7 +86,7 @@ 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, llm_model):
+    def check_health(self, content):
         cleaned_content = clean_awr_content(content)
         if len(cleaned_content) > 128000:
             cleaned_content = cleaned_content[:128000] + "\n\n[TRUNCATED]..."
@@ -94,25 +94,41 @@ class HealthAgent:
         prompt = f"""
 You are the Oracle AWR Health Analysis Agent.
 
-Your primary responsibility is to detect and report any possible database health risks or failures from the AWR report.
+Your primary responsibility is to detect and report ANY and ALL database health risks, alerts, warnings, or failures in the AWR report.
 
 You MUST:
-- Identify all mentions of warnings, failures, critical issues, and alerts.
-- Do not omit any mention of failure or warnings, even minor ones.
-- Clearly classify them as CRITICAL, WARNING, or INFO.
-- Provide suggested actions for CRITICAL and WARNING level issues.
+- Identify all issues marked as CRITICAL, WARNING, ALERT, FAILED, OFFLINE, CONFINED, DROPPED, or ERROR.
+- Never omit or generalize. If something appears important, call it out.
+- Classify each issue into: 🚨 CRITICAL / ⚠️ WARNING / ✅ INFO
+- For CRITICAL and WARNING, provide suggested actions or considerations.
 - 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.
 
-If Flash Cache, I/O, Smart Scan, ASM, Redo log, or RAC interconnect issues are mentioned, they MUST be called out explicitly.
+Specifically analyze Exadata Alerts Summary and Exadata Alerts Detail
+
+Example Output:
+🚨 CRITICAL
+- [issue]
+⚠️ WARNING
+- [issue]
+✅ INFO
+- No other warnings or critical issues detected.
 
 AWR CONTENT:
 {cleaned_content}
 """
 
         response = client.chat.completions.create(
-            model=llm_model,
+            model="gpt-4-turbo",
             messages=[
                 {"role": "system", "content": "You are the strict Oracle AWR Health Analysis Agent."},
                 {"role": "user", "content": prompt}
@@ -121,6 +137,7 @@ AWR CONTENT:
 
         return response.choices[0].message.content.strip()
 
+
 class RaterAgent:
     def rate(self, content, llm_model):
         prompt = f"Rate the following analysis from 1-5 stars and explain:\n\n{content}"