Create app.py

app.py

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+# === Imports ===
+import os
+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
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.lib.pagesizes import letter
+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:
+    raise ValueError("OPENAI_API_KEY environment variable is not set.")
+
+client = openai.OpenAI(api_key=openai_api_key)
+
+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"),
+    "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")
+
+try:
+    object_storage = oci.object_storage.ObjectStorageClient(oci_config)
+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}},
+    "X8": {"Quarter Rack": {"max_iops": 380000, "max_throughput": 28}, "Half Rack": {"max_iops": 760000, "max_throughput": 56}, "Full Rack": {"max_iops": 1520000, "max_throughput": 112}},
+    "X9": {"Quarter Rack": {"max_iops": 450000, "max_throughput": 30}, "Half Rack": {"max_iops": 900000, "max_throughput": 60}, "Full Rack": {"max_iops": 1800000, "max_throughput": 120}},
+    "X10": {"Quarter Rack": {"max_iops": 500000, "max_throughput": 35}, "Half Rack": {"max_iops": 1000000, "max_throughput": 70}, "Full Rack": {"max_iops": 2000000, "max_throughput": 140}},
+    "X11M": {"Quarter Rack": {"max_iops": 600000, "max_throughput": 40}, "Half Rack": {"max_iops": 1200000, "max_throughput": 80}, "Full Rack": {"max_iops": 2400000, "max_throughput": 160}},
+}
+
+# --- Supported LLM Models ---
+supported_llms = {
+    "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",
+    "gpt-4.1": "Great for quick coding and analysis",
+}
+
+# --- Utils ---
+def clean_awr_content(content):
+    if "<html" in content.lower():
+        soup = BeautifulSoup(content, "html.parser")
+        return soup.get_text()
+    return content
+
+def upload_awr_file(file_obj):
+    filename = os.path.basename(file_obj)
+    with open(file_obj, "rb") as f:
+        content = f.read()
+    object_storage.put_object(namespace, bucket_name, filename, content)
+    return f"\u2705 Uploaded {filename}"
+
+def list_awr_files():
+    try:
+        objects = object_storage.list_objects(namespace, bucket_name)
+        return [obj.name for obj in objects.data.objects if obj.name.endswith(".html") or obj.name.endswith(".txt")]
+    except Exception as e:
+        return [f"Error listing objects: {str(e)}"]
+
+def get_awr_file_text(filename):
+    try:
+        response = object_storage.get_object(namespace, bucket_name, filename)
+        raw = response.data.content.decode()
+        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
+    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):
+    if not file_list:
+        return "No files selected."
+    combined_text = ""
+    for fname in file_list:
+        content = get_awr_file_text(fname)
+        combined_text += f"\n=== AWR: {fname} ===\n{content[:3000]}...\n"
+    prompt = f"""You are a senior Oracle performance engineer. You will compare multiple AWR reports and highlight:
+- Key differences in workload or system behavior
+- Major trends or anomalies
+- Which report shows better performance and why
+- Exadata-specific metrics like Smart Scan, Flash I/O
+- Suggestions to unify or improve system behavior
+AWR Reports:
+{combined_text}
+"""
+    response = client.chat.completions.create(
+        model=llm_model,
+        messages=[
+            {"role": "system", "content": "You are a comparative AWR analysis expert."},
+            {"role": "user", "content": prompt}
+        ]
+    )
+    return response.choices[0].message.content.strip()
+
+# === 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 ========
+{cleaned_content}
+======== AWR REPORT END ========
+Required Output:
+- Performance Summary (with metric values)
+- Detailed Bottlenecks + Risks (quantified)
+- Forecast + Predictions
+- Monitoring Recommendations
+- Exadata Statistics (IO, Flash Cache, Smart Scan)
+- Recommended Next Steps to Bridge Gaps
+"""
+        if performance_test_mode and exadata_model and rack_size:
+            specs = exadata_specs.get(exadata_model, {}).get(rack_size, {})
+            if specs:
+                prompt += f"""
+This was a PERFORMANCE TEST on Oracle Exadata {exadata_model} {rack_size}.
+Theoretical Max:
+- IOPS: {specs['max_iops']}
+- Throughput: {specs['max_throughput']} GB/s
+Compare observed vs theoretical. Recommend actions to close the performance gap.
+"""
+        response = client.chat.completions.create(
+            model=llm_model,
+            messages=[
+                {"role": "system", "content": "You are an expert Oracle DBA."},
+                {"role": "user", "content": prompt}
+            ]
+        )
+        return response.choices[0].message.content.strip()
+
+class HealthAgent:
+    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]..."
+
+        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:
+- 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
+- I/O Subsystem stalls or errors
+- ASM/Grid Disk issues
+- Smart Scan failures
+- Redo Log issues
+- RAC Interconnect issues
+AWR CONTENT:
+{cleaned_content}
+"""
+        response = client.chat.completions.create(
+            model=llm_model,
+            messages=[
+                {"role": "system", "content": "You are the strict Oracle AWR Health Analysis Agent."},
+                {"role": "user", "content": prompt}
+            ]
+        )
+        return response.choices[0].message.content.strip()
+
+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",
+            messages=[{"role": "user", "content": prompt}]
+        )
+        return response.choices[0].message.content.strip()
+
+# === MAIN AWR PROCESS ===
+def process_awr(awr_text, threshold, performance_test_mode, exadata_model, rack_size, llm_model):
+    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.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")
+        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
+        )
+
+if __name__ == "__main__":
+    demo.launch(debug=True)
+
+
+