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| # === Imports === | |
| import os | |
| import re | |
| import gradio as gr | |
| import openai | |
| import oci | |
| from datetime import datetime | |
| from bs4 import BeautifulSoup | |
| # --- 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 Config --- | |
| #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"), | |
| #} | |
| # === 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) | |
| namespace = os.environ.get("OCI_NAMESPACE") | |
| bucket_name = os.environ.get("OCI_BUCKET_NAME") | |
| object_storage = oci.object_storage.ObjectStorageClient(oci_config) | |
| # --- 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", | |
| } | |
| # --- 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.name) | |
| content = file_obj.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 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() | |
| # === Gradio UI === | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Manual AWR Analysis"): | |
| gr.Markdown("# \U0001f9e0 Multi-Agent Oracle AWR Analyzer (Production Edition)") | |
| awr_text = gr.Textbox(label="Paste AWR Report", lines=30) | |
| 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") | |
| def toggle_visibility(mode): | |
| return gr.update(visible=mode), gr.update(visible=mode) | |
| 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") | |
| from your_existing_code import process_awr # Replace with actual import or include function here | |
| analyze_btn.click(process_awr, | |
| inputs=[awr_text, threshold, performance_test_mode, exadata_model, rack_size, llm_selector], | |
| outputs=[output, health, rating, retry_status]) | |
| 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("\U0001f503 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) | |