Create app.py

app.py

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+# --- Imports ---
+import os
+import re
+import gradio as gr
+import openai
+from datetime import datetime
+from bs4 import BeautifulSoup
+
+# --- API Keys + Colab support ---
+import os
+
+openai_api_key = os.environ.get("OPENAI_API_KEY")
+openrouter_key = os.environ.get("OPENROUTER")
+
+if not openai_api_key:
+    raise ValueError("OPENAI_API_KEY environment variable is not set.")
+if not openrouter_key:
+    raise ValueError("OPENROUTER environment variable is not set.")
+
+
+client = openai.OpenAI(api_key=openai_api_key)
+openai_rater = openai.OpenAI(api_key=openrouter_key, base_url="https://openrouter.ai/api/v1")
+
+# --- Logger ---
+log_filename = "rating_log.txt"
+if not os.path.exists(log_filename):
+    with open(log_filename, "w", encoding="utf-8") as f:
+        f.write("=== Rating Log Initialized ===\n")
+
+# --- 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}},
+}
+
+# --- Preprocessor ---
+def clean_awr_content(content):
+    if "<html" in content.lower():
+        soup = BeautifulSoup(content, "html.parser")
+        text = soup.get_text()
+    else:
+        text = content
+    cleaned = "\n".join([line.strip() for line in text.splitlines() if line.strip()])
+    return cleaned
+
+# --- AWR Analyzer ---
+def analyze_awr(content, performance_test_mode, exadata_model, rack_size):
+    cleaned_content = clean_awr_content(content)
+    max_chars = 128000
+    if len(cleaned_content) > max_chars:
+        cleaned_content = cleaned_content[:max_chars] + "\n\n[TRUNCATED]..."
+
+    # Build prompt
+    prompt = f"""You are an expert Oracle Database performance analyst with deep knowledge of AWR reports and the Time Scale Methodology.
+
+Analyze the following AWR Report:
+
+======== AWR REPORT START ========
+{cleaned_content}
+======== AWR REPORT END ========
+
+Please provide:
+- Performance Summary
+- Detailed Analysis of Bottlenecks and/or Degradation Risks
+- Performance Forecast and Predictions
+- Specific recommendations for Monitoring relative to bottlenecks or degradation risks
+- Provide a separate Exadata Statistics Performance Summary IO, Flash Cache, and Smart Scan utilization 
+"""
+
+    # Add Exadata comparison if performance test mode
+    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:
+- Max IOPS: {specs['max_iops']}
+- Max Throughput: {specs['max_throughput']} GB/s
+
+Show actual vs theoretical and generate Recommended Next Steps to Bridge Performance Gap.
+"""
+
+    # --- Call GPT-4o (or turbo) ---
+    MODEL = "gpt-4-turbo"  # BEST (or change to gpt-4-turbo if needed)
+
+    response = client.chat.completions.create(
+        model=MODEL,
+        messages=[
+            {"role": "system", "content": "You are an expert Oracle Database performance analyst."},
+            {"role": "user", "content": prompt}
+        ]
+    )
+
+    return response.choices[0].message.content.strip()
+
+
+
+# --- Rater ---
+def rate_answer_rater(question, final_answer):
+    prompt = f"Rate this answer 1-5 stars with explanation:\n\n{final_answer}"
+    response = openai_rater.chat.completions.create(
+        model="mistral/ministral-8b",
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return response.choices[0].message.content.strip()
+
+# --- Main Logic ---
+def process_awr(awr_text, correctness_threshold, performance_test_mode, exadata_model, rack_size):
+    if not awr_text.strip():
+        return "No AWR report provided.", "", ""
+
+    answer = analyze_awr(awr_text, performance_test_mode, exadata_model, rack_size)
+    rating_text = rate_answer_rater("AWR Analysis", answer)
+
+    stars = 0
+    match = re.search(r"(\d+)", rating_text)
+    if match:
+        stars = int(match.group(1))
+
+    if stars < correctness_threshold:
+        answer_retry = analyze_awr(awr_text, performance_test_mode, exadata_model, rack_size)
+        rating_text_retry = rate_answer_rater("AWR Analysis (Retry)", answer_retry)
+
+        with open(log_filename, "a", encoding="utf-8") as log_file:
+            log_file.write(f"\n---\n{datetime.now()} RETRY\nOriginal: {answer}\nRating: {rating_text}\nRetry: {answer_retry}\nRetry Rating: {rating_text_retry}\n")
+
+        return answer_retry, rating_text_retry, "✅ Retry Occurred (rating below threshold)"
+    else:
+        with open(log_filename, "a", encoding="utf-8") as log_file:
+            log_file.write(f"\n---\n{datetime.now()} SUCCESS\nAnswer: {answer}\nRating: {rating_text}\n")
+
+        return answer, rating_text, "✅ Accepted on first try"
+
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("## 📊 Oracle AWR Analyzer (AI + Rating + Retry + Exadata Gap Analysis)")
+
+    awr_text = gr.Textbox(label="Paste AWR Report (HTML or TXT)", lines=30, placeholder="Paste full AWR here...")
+    threshold = gr.Slider(0, 5, value=3, step=1, label="Correctness Threshold (Stars for Retry)")
+    performance_test_mode = gr.Checkbox(label="Performance Test Mode")
+    exadata_model = gr.Dropdown(choices=["X7", "X8", "X9", "X10", "X11M"], label="Exadata Model", visible=False)
+    rack_size = gr.Dropdown(choices=["Quarter Rack", "Half Rack", "Full Rack"], label="Rack Size", visible=False)
+
+    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")
+    output = gr.Textbox(label="AWR Analysis Result", lines=15)
+    rating = gr.Textbox(label="Rater Rating + Explanation", lines=4)
+    retry_status = gr.Textbox(label="Retry Status")
+
+    analyze_btn.click(process_awr, inputs=[awr_text, threshold, performance_test_mode, exadata_model, rack_size], outputs=[output, rating, retry_status])
+
+demo.launch(debug=True)