import os
import requests
import pandas as pd
from tools.summarizer import summarize_outliers, describe_dataset
from tools.synthetic_data import generate_synthetic_data
from tools.outlier_detection import detect_outliers
from tools.plotter import plot_outliers
from tools.vector_store import log_to_chromadb
OLLAMA_URL = "http://localhost:11434/api/generate"
MODEL = "llama3.2:1b"
def call_ollama(prompt: str) -> str:
"""Call the local LLM via Ollama with a structured prompt."""
response = requests.post(
OLLAMA_URL,
json={
"model": MODEL,
"prompt": prompt,
"temperature": 0.3,
"stream": False,
},
)
response.raise_for_status()
return response.json()["response"].strip()
def plan_and_recommend(input: str = "") -> str:
"""End-to-end agentic analysis with LLM recommendation."""
data_path = "data/generated_data.csv"
if not os.path.exists(data_path):
df = generate_synthetic_data()
df.to_csv(data_path, index=False)
else:
df = pd.read_csv(data_path)
df = detect_outliers(df, features=["feature_1", "feature_2"])
df.to_csv(data_path, index=False)
outlier_summary = summarize_outliers(df)
stats_summary = describe_dataset(df)
planner_prompt = (
"You are an autonomous data analyst. Based on the outlier analysis and data stats below, "
"recommend the next course of action.\n\n"
f"--- Outlier Summary ---\n{outlier_summary}\n\n"
f"--- Data Stats ---\n{stats_summary}\n\n"
"What should be done next? Options may include removing outliers, tuning the threshold, "
"running another model, or logging findings."
)
recommendation = call_ollama(planner_prompt)
log_to_chromadb(df)
return (
f"📊 Outlier Summary:\n{outlier_summary}\n\n"
f"📈 Dataset Stats:\n{stats_summary}\n\n"
f"🤖 LLM Recommendation:\n{recommendation}"
)
