Create app.py

app.py

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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)