Gui Vizsualition for SQLite Working

backend.py

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+from datetime import datetime, timedelta
+import time
+from watchdog.observers import Observer
+from watchdog.events import FileSystemEventHandler
+import os
+from fastapi import FastAPI, Request, WebSocket, WebSocketDisconnect, HTTPException
+from pydantic import BaseModel
+import pandas as pd
+import json
+from pathlib import Path
+from azure.identity import DefaultAzureCredential
+from mlos_bench.storage import from_config
+from copy import deepcopy
+import subprocess
+import logging
+import asyncio
+from fastapi.middleware.cors import CORSMiddleware
+import re
+import json5
+
+app = FastAPI()
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+# Load the storage config and connect to the storage
+try:
+    storage = storage = from_config(config="storage/sqlite.jsonc")
+except Exception as e:
+    raise HTTPException(status_code=500, detail=f"Error loading storage configuration: {e}")
+
+
+@app.get("/experiments")
+def get_experiments():
+    return list(storage.experiments.keys())
+
+
+@app.get("/experiment_results/{experiment_id}")
+def get_experiment_results(experiment_id: str):
+    try:
+        exp = storage.experiments[experiment_id]
+        return exp.results_df.to_dict(orient="records")
+    except KeyError:
+        raise HTTPException(status_code=404, detail="Experiment not found")
+
+
+def count_categorical_values(df: pd.DataFrame) -> str:
+    categorical_counts = {}
+    for col in df.select_dtypes(include=["object", "category"]).columns:
+        counts = df[col].value_counts().to_dict()
+        categorical_counts[col] = counts
+
+    count_str = "Categorical Counts:\n"
+    for col, counts in categorical_counts.items():
+        count_str += f"{col}:\n"
+        for value, count in counts.items():
+            count_str += f"  {value}: {count}\n"
+
+    return count_str
+
+
+# Load credentials from the JSON file
+try:
+    with open("azure_openai_credentials.json", "r") as file:
+        credentials = json.load(file)
+except Exception as e:
+    print("Error no open ai cred")
+
+# Try to create the AzureOpenAI client
+try:
+    client = AzureOpenAI(
+        azure_endpoint=credentials["azure_endpoint"],
+        api_key=credentials["api_key"],
+        api_version=credentials["api_version"],
+    )
+except Exception as e:
+    print("Error creating AzureOpenAI client:", e)
+
+
+class ExperimentExplanationRequest(BaseModel):
+    experiment_id: str
+
+
+@app.post("/get_experiment_explanation")
+def get_experiment_explanation(request: ExperimentExplanationRequest):
+    experiment_id = request.experiment_id
+    try:
+        exp = storage.experiments[experiment_id]
+        # Taking only the first 10 rows for simplicity
+        df = exp.results_df.tail(10)
+        experiment_data = df.to_dict(orient="records")
+
+        df_head = exp.results_df.head(10)
+        experiment_data_head = df_head.to_dict(orient="records")
+
+        df_des = exp.results_df.describe()
+        experiment_data_des = df_des.to_dict(orient="records")
+
+        count_str = count_categorical_values(df)
+
+        prompt = f"Explain the following experiment data: First 10 rows {experiment_data_head} last 10 {experiment_data} & descriptive stats {experiment_data_des} & categorical vars counts {count_str}. Give me params to complement config. params present in the data. Also explain what each param does and params for MySQL config that would complement what we have and can boost preformance if tuned. Explain which are dangreous to tune as it might fail the server. Also talk about parameters that are safe to tune. Talk about each in list format so that you are listing all information relevant to a param under its name"
+
+        response = client.chat.completions.create(
+            model="gpt4o",  # model = "deployment_name".
+            messages=[{"role": "assistant", "content": prompt}],
+            max_tokens=1000,
+        )
+
+        explanation = response.choices[0].message.content.strip()
+        print(explanation)
+        return {"explanation": explanation}
+    except KeyError:
+        raise HTTPException(status_code=404, detail="Experiment not found")
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000, reload=True)