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import json
import streamlit as st
import pandas as pd
from src.agent.graph import run_agent
from src.core.cleaning import clean_dataframe
from src.core.query import plan_query_with_llm, execute_query, summarize_results_with_llm
from src.core.security import basic_injection_check
st.set_page_config(page_title="AI Data Validation Agent (Industry-ready)", page_icon="🤖", layout="wide")
st.title("🤖 AI Data Validation Agent (Industry-ready)")
st.caption("Deterministic pandas answers + LLM planning/summarization (no LLM guessing).")
with st.sidebar:
st.header("Configuration")
api_key = st.text_input("OpenAI API Key", value=os.getenv("OPENAI_API_KEY",""), type="password")
model = st.selectbox("Model", ["gpt-4.1-mini","gpt-4.1","gpt-4o-mini","gpt-4o"], index=0)
st.divider()
uploaded = st.file_uploader("Upload CSV", type=["csv"])
st.divider()
st.markdown("**Tip:** Clean the data first, then ask questions.")
if "df_raw" not in st.session_state:
st.session_state.df_raw = None
if "df_clean" not in st.session_state:
st.session_state.df_clean = None
if "clean_report" not in st.session_state:
st.session_state.clean_report = None
tab1, tab2, tab3 = st.tabs(["📄 Data cleaning", "💬 Ask questions", "🧠 Self-correcting agent"])
with tab1:
st.subheader("1) Upload & clean")
if uploaded is None:
st.info("Upload a CSV from the sidebar.")
else:
df = pd.read_csv(uploaded)
st.session_state.df_raw = df
st.write("Raw preview")
st.dataframe(df.head(20), use_container_width=True)
if st.button("Clean & normalize", type="primary"):
dfc, report = clean_dataframe(df)
st.session_state.df_clean = dfc
st.session_state.clean_report = report
if st.session_state.df_clean is not None:
st.success("Cleaned dataset ready ✅")
report = st.session_state.clean_report
st.write("Cleaning report")
st.json({"rows": report.rows, "fixes": report.fixes, "warnings": report.warnings})
st.write("Clean preview")
st.dataframe(st.session_state.df_clean, use_container_width=True)
csv_bytes = st.session_state.df_clean.to_csv(index=False).encode("utf-8")
st.download_button("Download cleaned CSV", data=csv_bytes, file_name="cleaned.csv", mime="text/csv")
with tab2:
st.subheader("2) Ask deterministic questions")
if st.session_state.df_clean is None:
st.warning("Clean your dataset first (Data cleaning tab).")
else:
question = st.text_input("Ask a question about the dataset", placeholder='e.g., "Names of users in Artificial Intelligence department"')
colA, colB = st.columns([1,1])
with colA:
run = st.button("Run query", type="primary")
with colB:
show_plan = st.checkbox("Show query plan (JSON)", value=False)
if run:
if not api_key:
st.error("Please add your OpenAI API key in the sidebar.")
elif not question.strip():
st.error("Please type a question.")
else:
blocked, msg = basic_injection_check(question)
if blocked:
st.error(msg)
else:
try:
spec = plan_query_with_llm(question, st.session_state.df_clean, api_key=api_key, model=model)
if show_plan:
st.code(spec.model_dump_json(indent=2), language="json")
result = execute_query(spec, st.session_state.df_clean)
st.write("Result table")
st.dataframe(result, use_container_width=True)
answer = summarize_results_with_llm(question, result, api_key=api_key, model=model)
st.markdown("### Answer")
st.write(answer)
except Exception as e:
st.error(str(e))
st.divider()
st.markdown("### Why this is accurate")
st.markdown("- LLM only creates a small JSON query plan.\n- Pandas executes it deterministically.\n- LLM only summarizes already computed results.")
from src.agent.graph import run_agent
import pandas as pd
with tab3:
st.subheader("Self-Correcting Data Validation Agent")
st.caption("Paste messy data → Extract JSON → Validate → Auto-correct retries → Final schema-perfect output (NO hallucination)")
raw = st.text_area("Paste messy employee data (any format)", height=220)
max_attempts = st.slider("Max retries", 1, 6, 3)
# --- 1) VISUALIZE STATE MACHINE (diagram) ---
st.markdown("### 🧭 State Machine (Extract → Validate → Correct → Finalize)")
dot = """
digraph G {
rankdir=LR;
node [shape=box, style="rounded,filled", color="#444444", fillcolor="#F4F6F8"];
Extract [label="extract"];
Validate [label="validate"];
Correct [label="correct"];
Finalize [label="finalize"];
Extract -> Validate;
Validate -> Finalize [label="pass OR max_retries"];
Validate -> Correct [label="fail AND retries_left"];
Correct -> Validate;
}
"""
try:
st.graphviz_chart(dot, use_container_width=True)
except Exception:
st.code(
"extract → validate → (pass) finalize\n"
" ↘ (fail) correct → validate (loop)\n",
language="text"
)
if st.button("Run Agent", type="primary"):
if not api_key:
st.error("Please add your OpenAI API key in the sidebar.")
st.stop()
if not raw.strip():
st.error("Paste some messy data first.")
st.stop()
with st.spinner("Running extract → validate → correct loop..."):
final_state = run_agent(raw, api_key=api_key, model=model, max_attempts=max_attempts)
log = final_state.get("log", [])
result = final_state.get("result")
# --- 3) CORRECTION COUNT SUMMARY (metrics) ---
# attempts used
attempts_used = max((x.get("attempt", 0) for x in log), default=0)
# counts
employees_n = len(result.get("employees", [])) if result else 0
rejected_n = len(result.get("rejected", [])) if result else 0
# how many correct steps happened
correct_steps = sum(1 for x in log if x.get("step") == "correct")
validate_fails = sum(1 for x in log if x.get("step") == "validate" and x.get("status") == "fail")
st.markdown("### 📊 Run Summary")
c1, c2, c3, c4 = st.columns(4)
c1.metric("Attempts used", attempts_used if attempts_used else 1)
c2.metric("Corrections", correct_steps)
c3.metric("Valid employees", employees_n)
c4.metric("Rejected records", rejected_n)
# Optional: show pass/fail clearly
if result is None:
st.error("Could not produce schema-valid JSON within retry limit.")
else:
st.success("Schema-valid output ✅")
# --- 2) BEFORE / AFTER COMPARISON ---
st.markdown("### 🔁 Before vs After")
left, right = st.columns(2)
with left:
st.markdown("#### Before (Raw Input)")
st.code(raw.strip(), language="text")
with right:
st.markdown("#### After (Schema Output)")
if result is None:
st.code(final_state.get("last_json_text", ""), language="json")
else:
st.code(json.dumps(result, indent=2, default=str), language="json")
# --- Correction Log (keep your existing) ---
st.markdown("### 🧾 Correction Log")
st.json(log)
# If failed, stop here
if result is None:
st.markdown("### Last JSON Attempt (debug)")
st.code(final_state.get("last_json_text", ""), language="json")
st.stop()
# -------- Valid employees table --------
st.markdown("### ✅ Valid Employees")
employees = result.get("employees", [])
if employees:
df_emp = pd.DataFrame(employees)
st.dataframe(df_emp, use_container_width=True)
else:
st.info("No valid employees extracted (all records were rejected).")
# -------- Rejected records table --------
st.markdown("### 🚫 Rejected Records (No hallucination)")
rejected = result.get("rejected", [])
if rejected:
rej_rows = []
for r in rejected:
rej_rows.append(
{
"raw_record": r.get("raw_record", ""),
"reasons": "; ".join(r.get("reasons", [])),
}
)
df_rej = pd.DataFrame(rej_rows)
st.dataframe(df_rej, use_container_width=True)
else:
st.info("No rejected records. Everything was schema-valid.")
# -------- Download --------
st.download_button(
"Download JSON",
data=json.dumps(result, indent=2, default=str),
file_name="validated_output.json",
mime="application/json",
)
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