Update app.py

app.py

@@ -4,7 +4,6 @@ import oci
 import re
 import gradio as gr
 import openai
-import oci
 from datetime import datetime
 from bs4 import BeautifulSoup
 from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
@@ -14,7 +13,6 @@ from reportlab.lib.enums import TA_CENTER
 from reportlab.lib import colors
 import tempfile
 
-
 # --- API Keys ---
 openai_api_key = os.environ.get("OPENAI_API_KEY")
 if not openai_api_key:
@@ -26,8 +24,6 @@ openrouter_key = os.environ.get("OPENROUTER")
 openrouter = openai.OpenAI(api_key=openrouter_key, base_url="https://openrouter.ai/api/v1")
 
 # --- OCI Object Storage: Explicit Fixed Configuration ---
-
-# === OCI Object Storage Setup ===
 oci_config = {
     "user": os.environ.get("OCI_USER"),
     "tenancy": os.environ.get("OCI_TENANCY"),
@@ -35,7 +31,6 @@ oci_config = {
     "region": os.environ.get("OCI_REGION"),
     "key_content": os.environ.get("OCI_PRIVATE_KEY")
 }
-
 namespace = os.environ.get("OCI_NAMESPACE")
 bucket_name = os.environ.get("OCI_BUCKET_NAME")
 
@@ -44,23 +39,6 @@ try:
 except Exception as e:
     print("Failed to initialize OCI Object Storage client:", e)
 
-"""
-# --- OCI Object Storage Setup ---
-oci_config = {
-    "user": os.environ.get("OCI_USER"),
-    "tenancy": os.environ.get("OCI_TENANCY"),
-    "fingerprint": os.environ.get("OCI_FINGERPRINT"),
-    "region": os.environ.get("OCI_REGION"),
-    "key_content": os.environ.get("OCI_PRIVATE_KEY")
-}
-namespace = os.environ.get("OCI_NAMESPACE")
-bucket_name = os.environ.get("OCI_BUCKET_NAME")
-os.environ["OCI_BUCKET_NAME"] = "OracleTANGO"
-try:
-    object_storage = oci.object_storage.ObjectStorageClient(oci_config)
-except Exception as e:
-    print("Failed to initialize OCI Object Storage client:", e)
-"""
 # --- Exadata Specs ---
 exadata_specs = {
     "X7": {"Quarter Rack": {"max_iops": 350000, "max_throughput": 25}, "Half Rack": {"max_iops": 700000, "max_throughput": 50}, "Full Rack": {"max_iops": 1400000, "max_throughput": 100}},
@@ -85,6 +63,21 @@ def clean_awr_content(content):
         return soup.get_text()
     return content
 
+def awr_file_to_text(file_obj):
+    if not file_obj:
+        return ""
+    filename = file_obj.name if hasattr(file_obj, "name") else str(file_obj)
+    try:
+        content = file_obj.read() if hasattr(file_obj, "read") else open(file_obj, "rb").read()
+    except Exception:
+        with open(file_obj, "rb") as f:
+            content = f.read()
+    try:
+        text = content.decode()
+    except Exception:
+        text = content.decode("latin-1")
+    return clean_awr_content(text)
+
 def upload_awr_file(file_obj):
     filename = os.path.basename(file_obj)
     with open(file_obj, "rb") as f:
@@ -106,62 +99,29 @@ def get_awr_file_text(filename):
         return clean_awr_content(raw)
     except Exception as e:
         return f"Error loading file: {str(e)}"
-    
+
 def generate_pdf(analysis_text, health_text, rating_text, retry_status_text):
-    # Temporary file path
     temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
     pdf_path = temp_file.name
-    
-    # PDF setup
     doc = SimpleDocTemplate(pdf_path, pagesize=letter)
     styles = getSampleStyleSheet()
     elements = []
-    
-    # Custom header style
-    header_style = ParagraphStyle(
-        name="HeaderStyle",
-        fontSize=16,
-        alignment=TA_CENTER,
-        textColor=colors.darkblue,
-        spaceAfter=14
-    )
-    
-    # Section title style
-    section_style = ParagraphStyle(
-        name="SectionHeader",
-        fontSize=14,
-        textColor=colors.darkred,
-        spaceAfter=8
-    )
-    
-    # Body text style
-    body_style = ParagraphStyle(
-        name="BodyStyle",
-        fontSize=10,
-        leading=14,
-        spaceAfter=10
-    )
-    
-    # Title
+    header_style = ParagraphStyle(name="HeaderStyle", fontSize=16, alignment=TA_CENTER, textColor=colors.darkblue, spaceAfter=14)
+    section_style = ParagraphStyle(name="SectionHeader", fontSize=14, textColor=colors.darkred, spaceAfter=8)
+    body_style = ParagraphStyle(name="BodyStyle", fontSize=10, leading=14, spaceAfter=10)
     elements.append(Paragraph("Oracle AWR Analyzer Report", header_style))
     elements.append(Spacer(1, 12))
-    
-    # Sections
     sections = [
         ("AWR Analysis", analysis_text),
         ("Health Agent Findings", health_text),
         ("Rater Output", rating_text),
         ("Retry Status", retry_status_text)
     ]
-    
     for title, content in sections:
         elements.append(Paragraph(title, section_style))
         elements.append(Paragraph(content.replace("\n", "<br/>"), body_style))
         elements.append(Spacer(1, 12))
-    
-    # Build PDF
     doc.build(elements)
-    
     return pdf_path
 
 def compare_awrs(file_list, llm_model):
@@ -189,13 +149,15 @@ AWR Reports:
     )
     return response.choices[0].message.content.strip()
 
+def toggle_visibility(mode):
+    return gr.update(visible=mode), gr.update(visible=mode)
+
 # === AGENTS ===
 class CriticalAnalyzerAgent:
     def analyze(self, content, performance_test_mode, exadata_model, rack_size, llm_model):
         cleaned_content = clean_awr_content(content)
         if len(cleaned_content) > 128000:
             cleaned_content = cleaned_content[:128000] + "\n\n[TRUNCATED]..."
-
         prompt = f"""You are an expert Oracle DBA performance analyst specialized in AWR + Exadata.
 Please perform advanced analysis on the following report:
 ======== AWR REPORT START ========
@@ -233,7 +195,6 @@ class HealthAgent:
         cleaned_content = clean_awr_content(content)
         if len(cleaned_content) > 128000:
             cleaned_content = cleaned_content[:128000] + "\n\n[TRUNCATED]..."
-
         prompt = f"""You are the Oracle AWR Health Analysis Agent.
 Your primary responsibility is to detect and report ANY and ALL database health risks, alerts, warnings, or failures in the AWR report.
 You MUST:
@@ -275,83 +236,55 @@ def process_awr(awr_text, threshold, performance_test_mode, exadata_model, rack_
     analyzer = CriticalAnalyzerAgent()
     health = HealthAgent()
     rater = RaterAgent()
-
     if not awr_text.strip():
         return "No AWR text provided", "", "", ""
-
     analysis = analyzer.analyze(awr_text, performance_test_mode, exadata_model, rack_size, llm_model)
     health_status = health.check_health(awr_text, llm_model)
     rating_text = rater.rate(analysis)
-
     stars = 0
     match = re.search(r"(\d+)", rating_text)
     if match:
         stars = int(match.group(1))
-
     retry_status = "✅ Accepted"
     if stars < threshold:
         analysis = analyzer.analyze(awr_text, performance_test_mode, exadata_model, rack_size, llm_model)
         rating_text = rater.rate(analysis)
         retry_status = "✅ Retry Occurred"
-
     return analysis, health_status, rating_text, retry_status
 
 # === Gradio UI ===
-with gr.Tab("Manual AWR Analysis"):
-    gr.Markdown("# Multi-Agent Oracle AWR Analyzer (Production Edition)")
-
-    awr_file = gr.File(label="Upload AWR Report (.html or .txt)", file_types=[".html", ".txt"])
-    awr_text = gr.Textbox(label="AWR Report (pasted or loaded)", lines=30)
-    awr_file.upload(awr_file_to_text, inputs=awr_file, outputs=awr_text)
-
-    threshold = gr.Slider(0, 5, value=3, step=1, label="Correctness Threshold (Stars)")
-    performance_test_mode = gr.Checkbox(label="Performance Test Mode")
-    exadata_model = gr.Dropdown(choices=list(exadata_specs.keys()), label="Exadata Model", visible=False)
-    rack_size = gr.Dropdown(choices=["Quarter Rack", "Half Rack", "Full Rack"], label="Rack Size", visible=False)
-    llm_selector = gr.Dropdown(choices=list(supported_llms.keys()), value="gpt-4-turbo", label="LLM Model")
-    
-    performance_test_mode.change(toggle_visibility, inputs=performance_test_mode, outputs=[exadata_model, rack_size])
-    analyze_btn = gr.Button("Analyze AWR Report")
-    output = gr.Textbox(label="AWR Analysis", lines=20)
-    health = gr.Textbox(label="Health Agent Findings", lines=10)
-    rating = gr.Textbox(label="Rater", lines=3)
-    retry_status = gr.Textbox(label="Retry Status")
-    
-    analyze_btn.click(
-        process_awr,
-        inputs=[awr_text, threshold, performance_test_mode, exadata_model, rack_size, llm_selector],
-        outputs=[output, health, rating, retry_status]
-    )
-    
-    # PDF Export
-    pdf_button = gr.Button("📄 Generate PDF")
-    pdf_file = gr.File(label="Download PDF", type="file")
-    pdf_button.click(
-        fn=generate_pdf,
-        inputs=[output, health, rating, retry_status],
-        outputs=pdf_file
-    )
-
+with gr.Blocks() as demo:
+    with gr.Tab("Manual AWR Analysis"):
+        gr.Markdown("# Multi-Agent Oracle AWR Analyzer (Version 3.1)")
+        awr_file = gr.File(label="Upload AWR Report (.html or .txt)", file_types=[".html", ".txt"])
+        awr_text = gr.Textbox(label="AWR Report (pasted or loaded)", lines=30)
+        awr_file.upload(awr_file_to_text, inputs=awr_file, outputs=awr_text)
+        threshold = gr.Slider(0, 5, value=3, step=1, label="Correctness Threshold (Stars)")
+        performance_test_mode = gr.Checkbox(label="Performance Test Mode")
+        exadata_model = gr.Dropdown(choices=list(exadata_specs.keys()), label="Exadata Model", visible=False)
+        rack_size = gr.Dropdown(choices=["Quarter Rack", "Half Rack", "Full Rack"], label="Rack Size", visible=False)
+        llm_selector = gr.Dropdown(choices=list(supported_llms.keys()), value="gpt-4.1", label="LLM Model")
+        performance_test_mode.change(toggle_visibility, inputs=performance_test_mode, outputs=[exadata_model, rack_size])
+        analyze_btn = gr.Button("Analyze AWR Report")
+        output = gr.Textbox(label="AWR Analysis", lines=20)
+        health = gr.Textbox(label="Health Agent Findings", lines=10)
+        rating = gr.Textbox(label="Rater", lines=3)
+        retry_status = gr.Textbox(label="Retry Status")
+        analyze_btn.click(process_awr, inputs=[awr_text, threshold, performance_test_mode, exadata_model, rack_size, llm_selector], outputs=[output, health, rating, retry_status])
+        pdf_button = gr.Button("📄 Generate PDF")
+        pdf_file = gr.File(label="Download PDF", type="file")
+        pdf_button.click(fn=generate_pdf, inputs=[output, health, rating, retry_status], outputs=pdf_file)
 
     with gr.Tab("Compare AWRs from OCI"):
         upload_file = gr.File(label="Upload AWR Report", file_types=[".html", ".txt"])
         upload_status = gr.Textbox(label="Upload Status")
         upload_file.upload(fn=upload_awr_file, inputs=upload_file, outputs=upload_status)
-
         refresh_button = gr.Button("🔃 Refresh File List")
         file_multiselect = gr.Dropdown(choices=[], label="Select AWR Files", multiselect=True)
         refresh_button.click(fn=lambda: gr.update(choices=list_awr_files()), outputs=file_multiselect)
-
-        llm_compare = gr.Dropdown(choices=list(supported_llms.keys()), value="gpt-4-turbo", label="LLM Model for Comparison")
+        llm_compare = gr.Dropdown(choices=list(supported_llms.keys()), value="gpt-4.1", label="LLM Model for Comparison")
         compare_output = gr.Textbox(label="Comparison Output", lines=20)
-        gr.Button("Compare Selected AWRs").click(
-            fn=compare_awrs,
-            inputs=[file_multiselect, llm_compare],
-            outputs=compare_output
-        )
+        gr.Button("Compare Selected AWRs").click(fn=compare_awrs, inputs=[file_multiselect, llm_compare], outputs=compare_output)
 
 if __name__ == "__main__":
     demo.launch(debug=True)
-
-
-