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# app.py
# app.py
import os
import io
import tempfile
import json
import math
import traceback
from typing import Optional, List, Tuple, Dict, Any, Union
import pandas as pd
import numpy as np
import gradio as gr
import seaborn as sns
from scipy import stats
import warnings
warnings.filterwarnings('ignore')
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder, LabelEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.feature_selection import RFECV
from sklearn.linear_model import LinearRegression, Ridge, Lasso, LogisticRegression
from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingRegressor, GradientBoostingClassifier
from sklearn.svm import SVR, SVC
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, accuracy_score, classification_report, confusion_matrix, roc_auc_score, roc_curve, silhouette_score, davies_bouldin_score, calinski_harabasz_score
from sklearn.cluster import KMeans, DBSCAN
from sklearn.decomposition import PCA
from sklearn.exceptions import NotFittedError
import scipy.stats as stats
import matplotlib
matplotlib.use("Agg") # for headless plotting
import matplotlib.pyplot as plt
# --- lightweight password hashing (stdlib) ---
import os as _os
import hashlib, binascii, hmac as _hmac
def generate_password_hash(password: str) -> str:
salt = _os.urandom(16)
dk = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000)
return binascii.hexlify(salt).decode() + ':' + binascii.hexlify(dk).decode()
def check_password_hash(stored_hash: str, password: str) -> bool:
try:
salt_hex, dk_hex = stored_hash.split(':')
except ValueError:
return False
salt = binascii.unhexlify(salt_hex)
dk = binascii.unhexlify(dk_hex)
new_dk = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000)
return _hmac.compare_digest(new_dk, dk)
# ------------------------------------------------
# -----------------------------
# Simple in-repo auth store (file-backed)
# -----------------------------
AUTH_STORE = "auth_store.json"
def _ensure_auth():
if not os.path.exists(AUTH_STORE):
admin_pass = generate_password_hash("password")
with open(AUTH_STORE, "w") as f:
json.dump({"admin": admin_pass}, f)
def authenticate(username: str, password: str) -> bool:
_ensure_auth()
with open(AUTH_STORE, "r") as f:
data = json.load(f)
stored = data.get(username)
if not stored:
return False
return check_password_hash(stored, password)
# seed auth
_ensure_auth()
# -----------------------------
# App constants & helpers
# -----------------------------
HIGH_CARD_THRESHOLD_COUNT = 50
HIGH_CARD_THRESHOLD_RATIO = 0.10
LOGO_PATH = "DataSynth.png"
# -----------------------------
# Small utility functions
# -----------------------------
def read_file_to_df(uploaded) -> pd.DataFrame:
if uploaded is None:
return pd.DataFrame()
try:
if hasattr(uploaded, "name") and os.path.exists(uploaded.name):
path = uploaded.name
elif isinstance(uploaded, dict) and "name" in uploaded:
path = uploaded["name"]
else:
content = uploaded.read()
return _read_bytes_content(content)
return _read_file_path(path)
except Exception as e:
print(f"read_file_to_df error: {e}")
try:
if 'path' in locals():
return pd.read_csv(path, encoding_errors='ignore', engine='python')
except Exception:
pass
raise
def _read_bytes_content(content) -> pd.DataFrame:
formats_to_try = [
('csv', lambda: pd.read_csv(io.BytesIO(content))),
('csv_utf8', lambda: pd.read_csv(io.BytesIO(content), encoding='utf-8')),
('csv_latin1', lambda: pd.read_csv(io.BytesIO(content), encoding='latin1')),
('csv_ignore', lambda: pd.read_csv(io.BytesIO(content), encoding_errors='ignore')),
('excel', lambda: pd.read_excel(io.BytesIO(content))),
('json', lambda: pd.read_json(io.BytesIO(content))),
]
for format_name, reader_func in formats_to_try:
try:
return reader_func()
except Exception as e:
print(f"Failed to read as {format_name}: {e}")
continue
raise ValueError("Could not read file with any supported format")
def _read_file_path(path) -> pd.DataFrame:
file_ext = os.path.splitext(path)[1].lower()
if file_ext in ['.csv', '.txt', '.tsv']:
encodings = [None, 'utf-8', 'latin1', 'cp1252', 'iso-8859-1']
for encoding in encodings:
try:
if file_ext == '.tsv':
return pd.read_csv(path, sep='\t', encoding=encoding, encoding_errors='ignore')
else:
return pd.read_csv(path, encoding=encoding, encoding_errors='ignore')
except Exception:
continue
elif file_ext in ['.xlsx', '.xls', '.xlsm', '.xlsb']:
return pd.read_excel(path)
elif file_ext == '.json':
return pd.read_json(path)
elif file_ext in ['.parquet']:
return pd.read_parquet(path)
elif file_ext in ['.feather']:
return pd.read_feather(path)
try:
return pd.read_csv(path, encoding_errors='ignore', engine='python')
except Exception:
return pd.read_excel(path)
def comprehensive_data_profile(df: pd.DataFrame) -> Dict[str, Any]:
if df is None or df.empty:
return {}
profile = {}
profile["rows"], profile["columns"] = df.shape
profile["memory_usage"] = df.memory_usage(deep=True).sum() / 1024**2
dtypes = df.dtypes.apply(lambda x: x.name).to_dict()
profile["dtypes"] = dtypes
nulls = df.isnull().sum().to_dict()
profile["nulls"] = nulls
profile["null_pct"] = {k: (v / len(df)) for k, v in nulls.items()}
unique_counts = df.nunique(dropna=False).to_dict()
profile["unique"] = unique_counts
profile["duplicate_rows"] = df.duplicated().sum()
profile["duplicate_pct"] = profile["duplicate_rows"] / len(df)
numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
categorical_cols = df.select_dtypes(include=["object", "category"]).columns.tolist()
datetime_cols = df.select_dtypes(include=["datetime64"]).columns.tolist()
profile["numeric_cols"] = numeric_cols
profile["categorical_cols"] = categorical_cols
profile["datetime_cols"] = datetime_cols
high_cardinality = []
for col, cnt in unique_counts.items():
if col in categorical_cols and (cnt > HIGH_CARD_THRESHOLD_COUNT or
(cnt / len(df) > HIGH_CARD_THRESHOLD_RATIO)):
high_cardinality.append(col)
profile["high_cardinality"] = high_cardinality
quantitative_stats = {}
for col in numeric_cols:
col_data = df[col].dropna()
if len(col_data) > 0:
quantitative_stats[col] = {
"mean": col_data.mean(),
"median": col_data.median(),
"std": col_data.std(),
"min": col_data.min(),
"max": col_data.max(),
"q1": col_data.quantile(0.25),
"q3": col_data.quantile(0.75),
"skew": col_data.skew(),
"kurtosis": col_data.kurtosis(),
"zeros": (col_data == 0).sum(),
"zeros_pct": (col_data == 0).sum() / len(col_data),
"outliers": detect_outliers_iqr(col_data)
}
profile["quantitative_stats"] = quantitative_stats
qualitative_stats = {}
for col in categorical_cols:
col_data = df[col].dropna()
if len(col_data) > 0:
value_counts = col_data.value_counts()
qualitative_stats[col] = {
"top_value": value_counts.index[0] if len(value_counts) > 0 else None,
"top_freq": value_counts.iloc[0] if len(value_counts) > 0 else 0,
"top_freq_pct": value_counts.iloc[0] / len(col_data) if len(value_counts) > 0 else 0,
"unique_values": len(value_counts),
"entropy": stats.entropy(value_counts.values) if len(value_counts) > 0 else 0
}
profile["qualitative_stats"] = qualitative_stats
quality_metrics = {
"completeness": (len(df) - df.isnull().sum().sum()) / (len(df) * len(df.columns)),
"uniqueness": 1 - (profile["duplicate_pct"]),
"validity": {}
}
profile["quality_metrics"] = quality_metrics
if len(numeric_cols) > 1:
profile["correlation_matrix"] = df[numeric_cols].corr().round(3).to_dict()
else:
profile["correlation_matrix"] = {}
profile["head"] = df.head(10).to_dict(orient="records")
return profile
def detect_outliers_iqr(series):
Q1 = series.quantile(0.25)
Q3 = series.quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
return ((series < lower_bound) | (series > upper_bound)).sum()
def profile_to_enhanced_markdown(profile: Dict[str, Any]) -> str:
if not profile:
return "No data loaded."
md = []
md.append("# 📊 Comprehensive Data Profile Report")
md.append("---")
md.append("## 🎯 Dataset Overview")
with io.StringIO() as overview:
overview.write(f"**Rows:** {profile['rows']:,} | ")
overview.write(f"**Columns:** {profile['columns']} | ")
overview.write(f"**Memory:** {profile['memory_usage']:.2f} MB | ")
overview.write(f"**Duplicates:** {profile['duplicate_rows']} ({profile['duplicate_pct']:.2%})")
md.append(overview.getvalue())
md.append("")
md.append("## 📈 Data Quality Scorecard")
quality = profile["quality_metrics"]
md.append(f"**Completeness:** {quality['completeness']:.2%} | ")
md.append(f"**Uniqueness:** {quality['uniqueness']:.2%}")
md.append("")
md.append("## 🗂️ Column Type Summary")
md.append(f"**Numeric:** {len(profile['numeric_cols'])} | ")
md.append(f"**Categorical:** {len(profile['categorical_cols'])} | ")
md.append(f"**DateTime:** {len(profile['datetime_cols'])}")
md.append("")
md.append("### 🔍 Detailed Column Analysis")
md.append("| Column | Type | Nulls | Null % | Unique | Completeness | Issues |")
md.append("|--------|------|-------|---------|--------|--------------|--------|")
for col in profile["dtypes"].keys():
dtype = profile["dtypes"][col]
nulls = profile["nulls"].get(col, 0)
null_pct = profile["null_pct"].get(col, 0)
uniq = profile["unique"].get(col, 0)
completeness = 1 - null_pct
issues = []
if null_pct > 0.5:
issues.append("🔴 High nulls")
elif null_pct > 0.2:
issues.append("🟡 Medium nulls")
if col in profile["high_cardinality"]:
issues.append("🔵 High cardinality")
if col in profile["numeric_cols"]:
stats = profile["quantitative_stats"].get(col, {})
outliers = stats.get("outliers", 0)
if outliers > 0:
issues.append("⚫ Outliers")
issues_str = ", ".join(issues) if issues else "✅ Good"
md.append(f"| {col} | {dtype} | {nulls} | {null_pct:.2%} | {uniq} | {completeness:.2%} | {issues_str} |")
md.append("")
if profile["quantitative_stats"]:
md.append("### 📈 Quantitative Columns Analysis")
md.append("| Column | Mean | Std | Min | Max | Skew | Outliers | Zeros |")
md.append("|--------|------|-----|-----|-----|------|----------|-------|")
for col, stats in profile["quantitative_stats"].items():
md.append(f"| {col} | {stats['mean']:.2f} | {stats['std']:.2f} | {stats['min']:.2f} | {stats['max']:.2f} | {stats['skew']:.2f} | {stats['outliers']} | {stats['zeros']} |")
md.append("")
if profile["qualitative_stats"]:
md.append("### 📊 Qualitative Columns Analysis")
md.append("| Column | Top Value | Top Freq | Top % | Unique | Entropy |")
md.append("|--------|-----------|----------|-------|--------|---------|")
for col, stats in profile["qualitative_stats"].items():
top_value = str(stats["top_value"])[:20] + "..." if len(str(stats["top_value"])) > 20 else str(stats["top_value"])
md.append(f"| {col} | {top_value} | {stats['top_freq']} | {stats['top_freq_pct']:.2%} | {stats['unique_values']} | {stats['entropy']:.2f} |")
md.append("")
if profile["high_cardinality"]:
md.append("### ⚠️ High Cardinality Columns")
md.append("The following columns have high cardinality (may impact modeling):")
for col in profile["high_cardinality"]:
md.append(f"- **{col}**: {profile['unique'][col]} unique values")
md.append("")
if profile["correlation_matrix"]:
md.append("### 🔗 Correlation Highlights")
corr_matrix = profile["correlation_matrix"]
numeric_cols = list(corr_matrix.keys())
strong_corrs = []
for i, col1 in enumerate(numeric_cols):
for j, col2 in enumerate(numeric_cols):
if i < j:
corr = abs(corr_matrix[col1][col2])
if corr > 0.7:
strong_corrs.append((col1, col2, corr_matrix[col1][col2]))
if strong_corrs:
md.append("**Strong Correlations (|r| > 0.7):**")
for col1, col2, corr in sorted(strong_corrs, key=lambda x: abs(x[2]), reverse=True):
md.append(f"- {col1} ↔ {col2}: {corr:.3f}")
else:
md.append("No strong correlations found among numeric columns.")
md.append("")
return "\n".join(md)
def create_distribution_plots(df: pd.DataFrame, profile: Dict[str, Any]):
numeric_cols = profile.get("numeric_cols", [])
categorical_cols = profile.get("categorical_cols", [])
plots = {}
for col in numeric_cols[:4]:
try:
fig = px.histogram(df, x=col, title=f"Distribution of {col}",
marginal="box", nbins=50)
plots[f"num_{col}"] = fig
except Exception as e:
print(f"Plot error for {col}: {e}")
for col in categorical_cols[:3]:
try:
value_counts = df[col].value_counts().head(10)
fig = px.bar(x=value_counts.index, y=value_counts.values,
title=f"Top 10 Values in {col}")
fig.update_layout(xaxis_title=col, yaxis_title="Count")
plots[f"cat_{col}"] = fig
except Exception as e:
print(f"Plot error for {col}: {e}")
return plots
def create_correlation_plot(df: pd.DataFrame, profile: Dict[str, Any]):
numeric_cols = profile.get("numeric_cols", [])
if len(numeric_cols) < 2:
return None
try:
corr_matrix = df[numeric_cols].corr()
fig = px.imshow(corr_matrix,
title="Correlation Matrix",
color_continuous_scale="RdBu_r",
aspect="auto")
return fig
except Exception as e:
print(f"Correlation plot error: {e}")
return None
# -----------------------------
# Data cleaning & feature engineering helpers
# -----------------------------
def drop_high_cardinality(df: pd.DataFrame, threshold_count=HIGH_CARD_THRESHOLD_COUNT, threshold_ratio=HIGH_CARD_THRESHOLD_RATIO):
n = len(df)
cols_to_drop = []
for c in df.columns:
if df[c].nunique(dropna=False) > threshold_count or (df[c].nunique(dropna=False)/max(1,n) > threshold_ratio):
if df[c].dtype == "object" or str(df[c].dtype).startswith("category"):
cols_to_drop.append(c)
return df.drop(columns=cols_to_drop, errors='ignore'), cols_to_drop
def impute_df(df: pd.DataFrame, numeric_strategy="mean", categorical_strategy="most_frequent", fill_value: Optional[str]=None):
df = df.copy()
num_cols = df.select_dtypes(include=[np.number]).columns.tolist()
cat_cols = df.select_dtypes(include=["object", "category"]).columns.tolist()
if num_cols:
imp = SimpleImputer(strategy=numeric_strategy)
df[num_cols] = imp.fit_transform(df[num_cols])
if cat_cols:
if categorical_strategy == "constant" and fill_value is not None:
imp2 = SimpleImputer(strategy="constant", fill_value=fill_value)
else:
imp2 = SimpleImputer(strategy=categorical_strategy)
df[cat_cols] = imp2.fit_transform(df[cat_cols])
return df
def treat_outliers_iqr(df: pd.DataFrame, cols: List[str], method="cap"):
df = df.copy()
for c in cols:
if c not in df.columns:
continue
if not np.issubdtype(df[c].dtype, np.number):
continue
q1 = df[c].quantile(0.25)
q3 = df[c].quantile(0.75)
iqr = q3 - q1
lower = q1 - 1.5 * iqr
upper = q3 + 1.5 * iqr
if method == "remove":
df = df[(df[c] >= lower) & (df[c] <= upper)]
elif method == "cap":
df[c] = np.where(df[c] < lower, lower, df[c])
df[c] = np.where(df[c] > upper, upper, df[c])
return df
def parse_dates(df: pd.DataFrame, col: str, fmt: Optional[str]=None):
df = df.copy()
try:
if fmt:
df[col] = pd.to_datetime(df[col], format=fmt, errors="coerce")
else:
df[col] = pd.to_datetime(df[col], errors="coerce", infer_datetime_format=True)
except Exception as e:
print("parse_dates", e)
return df
def text_clean(df: pd.DataFrame, cols: List[str], lower=True, strip=True):
df = df.copy()
for c in cols:
if c not in df.columns:
continue
df[c] = df[c].astype(str)
if strip:
df[c] = df[c].str.strip()
if lower:
df[c] = df[c].str.lower()
return df
def transform_cols(df: pd.DataFrame, cols: List[str], method="log"):
df = df.copy()
for c in cols:
if c in df.columns and np.issubdtype(df[c].dtype, np.number):
if method == "log":
df[c] = df[c].apply(lambda x: np.log(x) if x>0 else x)
elif method == "sqrt":
df[c] = df[c].apply(lambda x: np.sqrt(x) if x>=0 else x)
return df
# -----------------------------
# Enhanced Data Preparation Functions
# -----------------------------
def update_column_lists(df):
def get_columns_safe(df):
if df is None:
return []
cols = list(df.columns)
if hasattr(df.columns, "levels") and hasattr(df.columns, "names"):
try:
cols = ['_'.join(map(str, c)) if isinstance(c, (list, tuple)) else str(c) for c in cols]
except Exception:
cols = [str(c) for c in cols]
else:
cols = [str(c) for c in cols]
return cols
cols = get_columns_safe(df)
single_value = cols[0] if cols else None
multi_value = cols if cols else []
return (
gr.update(choices=cols, value=multi_value),
gr.update(choices=cols, value=single_value),
gr.update(choices=cols, value=single_value),
gr.update(choices=cols, value=multi_value),
gr.update(choices=cols, value=multi_value),
gr.update(choices=cols, value=multi_value),
gr.update(choices=cols, value=multi_value),
gr.update(choices=cols, value=single_value),
gr.update(choices=cols, value=single_value),
gr.update(choices=cols, value=multi_value),
gr.update(choices=cols, value=multi_value),
gr.update(choices=cols, value=multi_value),
gr.update(choices=cols, value=multi_value),
gr.update(choices=cols, value=multi_value),
gr.update(choices=cols, value=single_value)
)
def apply_column_operations(df, selected_cols, rename_mapping, dtype_conversions):
df = df.copy()
if selected_cols:
df = df[selected_cols]
for old_name, new_name in rename_mapping.items():
if old_name in df.columns:
df = df.rename(columns={old_name: new_name})
for col, target_dtype in dtype_conversions.items():
if col in df.columns:
try:
if target_dtype == "numeric":
df[col] = pd.to_numeric(df[col], errors='coerce')
elif target_dtype == "integer":
df[col] = pd.to_numeric(df[col], errors='coerce').astype('Int64')
elif target_dtype == "float":
df[col] = pd.to_numeric(df[col], errors='coerce').astype(float)
elif target_dtype == "datetime":
df[col] = pd.to_datetime(df[col], errors='coerce')
elif target_dtype == "category":
df[col] = df[col].astype('category')
elif target_dtype == "boolean":
df[col] = df[col].astype(bool)
except Exception as e:
print(f"Error converting {col} to {target_dtype}: {e}")
return df
def detect_high_cardinality_cols(df, threshold):
high_card_cols = []
for col in df.columns:
if df[col].dtype in ['object', 'category']:
unique_count = df[col].nunique()
if unique_count > threshold:
high_card_cols.append((col, unique_count))
return high_card_cols
def advanced_imputation(df, num_cols, num_method, num_custom, cat_cols, cat_method, cat_custom):
df = df.copy()
if num_cols:
for col in num_cols:
if col in df.columns:
if num_method == "Mean":
df[col].fillna(df[col].mean(), inplace=True)
elif num_method == "Median":
df[col].fillna(df[col].median(), inplace=True)
elif num_method == "Mode":
df[col].fillna(df[col].mode()[0] if not df[col].mode().empty else 0, inplace=True)
elif num_method == "Zero":
df[col].fillna(0, inplace=True)
elif num_method == "Custom Value" and num_custom:
try:
custom_val = float(num_custom)
df[col].fillna(custom_val, inplace=True)
except ValueError:
df[col].fillna(0, inplace=True)
elif num_method in ["Forward Fill", "LOCF"]:
df[col].fillna(method='ffill', inplace=True)
elif num_method in ["Backward Fill", "NOCB"]:
df[col].fillna(method='bfill', inplace=True)
elif num_method == "Linear Interpolation":
df[col].interpolate(method='linear', inplace=True)
if cat_cols:
for col in cat_cols:
if col in df.columns:
if cat_method == "Mode":
df[col].fillna(df[col].mode()[0] if not df[col].mode().empty else "Unknown", inplace=True)
elif cat_method == "Most Frequent":
df[col].fillna(df[col].value_counts().index[0] if len(df[col].value_counts()) > 0 else "Unknown", inplace=True)
elif cat_method == "Arbitrary ('Unknown')":
df[col].fillna("Unknown", inplace=True)
elif cat_method == "Constant Value" and cat_custom:
df[col].fillna(cat_custom, inplace=True)
return df
def apply_text_operations(df, text_cols, operations):
df = df.copy()
for col in text_cols:
if col in df.columns:
df[col] = df[col].astype(str)
for op in operations:
if op == "Remove leading/trailing whitespace":
df[col] = df[col].str.strip()
elif op == "Convert to lowercase":
df[col] = df[col].str.lower()
elif op == "Convert to uppercase":
df[col] = df[col].str.upper()
elif op == "Remove special characters":
df[col] = df[col].str.replace(r'[^\w\s]', '', regex=True)
elif op == "Remove numbers":
df[col] = df[col].str.replace(r'\d+', '', regex=True)
elif op == "Remove extra spaces":
df[col] = df[col].str.replace(r'\s+', ' ', regex=True)
return df
def split_text_column(df, col, delimiter):
if col not in df.columns:
return df, "Column not found"
df = df.copy()
try:
split_df = df[col].str.split(delimiter, expand=True)
new_col_names = [f"{col}_{i+1}" for i in range(split_df.shape[1])]
split_df.columns = new_col_names
df = pd.concat([df, split_df], axis=1)
return df, f"Successfully split {col} into {len(new_col_names)} columns"
except Exception as e:
return df, f"Error splitting column: {str(e)}"
def create_formula_column(df, formula, new_col_name):
df = df.copy()
try:
if "=" in formula:
formula = formula.split("=")[1].strip()
formula = formula.replace("CURRENT_YEAR", str(pd.Timestamp.now().year))
try:
df[new_col_name] = df.eval(formula)
return df, f"Successfully created column '{new_col_name}'"
except:
df[new_col_name] = formula
return df, f"Created column '{new_col_name}' with constant value"
except Exception as e:
return df, f"Error creating formula column: {str(e)}"
# -----------------------------
# Visualization NLP
# -----------------------------
def nlp_to_chart_instruction(query: str, df: pd.DataFrame):
q = query.lower()
numeric = df.select_dtypes(include=[np.number]).columns.tolist()
categorical = df.select_dtypes(include=["object", "category"]).columns.tolist()
tokens = q.split()
if "hist" in q or "histogram" in q or "distribution" in q:
for c in numeric:
if c.lower() in q:
return ("hist", [c])
if numeric:
return ("hist", [numeric[0]])
if "scatter" in q or "vs" in q or "versus" in q:
for c in numeric:
if c.lower() in q:
x = c
for d in numeric:
if d!=c and d.lower() in q:
return ("scatter", [x, d])
if len(numeric)>1:
return ("scatter", [numeric[0], numeric[1]])
if len(numeric)>=2:
return ("scatter", [numeric[0], numeric[1]])
if "bar" in q or "count" in q or "counts" in q or "value counts" in q:
for c in categorical:
if c.lower() in q:
return ("bar", [c])
if categorical:
return ("bar", [categorical[0]])
if "box" in q or "outlier" in q:
for c in numeric:
if c.lower() in q:
return ("box", [c])
if numeric:
return ("box", [numeric[0]])
return ("table", [])
def render_chart_from_instruction(instruction: Tuple[str, List[str]], df: pd.DataFrame):
typ, cols = instruction
if typ == "hist":
c = cols[0]
fig = px.histogram(df, x=c, title=f"Distribution of {c}")
return fig
if typ == "scatter":
x, y = cols[:2]
fig = px.scatter(df, x=x, y=y, title=f"{y} vs {x}")
return fig
if typ == "bar":
c = cols[0]
vc = df[c].value_counts().reset_index()
vc.columns = [c, "count"]
fig = px.bar(vc, x=c, y="count", title=f"Counts of {c}")
return fig
if typ == "box":
c = cols[0]
fig = px.box(df, y=c, title=f"Box plot of {c}")
return fig
return None
# -----------------------------
# Modeling - Regression & Classification
# -----------------------------
REGRESSION_MODELS = {
"Linear Regression": LinearRegression,
"Ridge Regression": Ridge,
"Lasso Regression": Lasso,
"Decision Tree Regressor": DecisionTreeRegressor,
"Random Forest Regressor": RandomForestRegressor,
"Gradient Boosting Regressor": GradientBoostingRegressor,
"Support Vector Regressor": SVR,
"K-Neighbors Regressor": KNeighborsRegressor
}
CLASSIFICATION_MODELS = {
"Logistic Regression": LogisticRegression,
"Decision Tree Classifier": DecisionTreeClassifier,
"Random Forest Classifier": RandomForestClassifier,
"Gradient Boosting Classifier": GradientBoostingClassifier,
"Support Vector Classifier": SVC,
"Gaussian Naive Bayes": GaussianNB,
"K-Neighbors Classifier": KNeighborsClassifier
}
# Model hyperparameter grids for tuning
MODEL_PARAM_GRIDS = {
"Random Forest Regressor": {
'model__n_estimators': [50, 100, 200],
'model__max_depth': [None, 10, 20],
'model__min_samples_split': [2, 5, 10]
},
"Random Forest Classifier": {
'model__n_estimators': [50, 100, 200],
'model__max_depth': [None, 10, 20],
'model__min_samples_split': [2, 5, 10]
},
"Gradient Boosting Regressor": {
'model__n_estimators': [50, 100],
'model__learning_rate': [0.01, 0.1, 0.2],
'model__max_depth': [3, 5, 7]
},
"Gradient Boosting Classifier": {
'model__n_estimators': [50, 100],
'model__learning_rate': [0.01, 0.1, 0.2],
'model__max_depth': [3, 5, 7]
},
"Logistic Regression": {
'model__C': [0.1, 1, 10],
'model__penalty': ['l2', 'none']
}
}
def prepare_features_targets(df: pd.DataFrame, target: str, drop_cols: List[str]=None, drop_high_card=True):
df = df.copy()
if drop_cols:
df = df.drop(columns=drop_cols, errors='ignore')
if drop_high_card:
df, dropped = drop_high_cardinality(df)
if target not in df.columns:
raise ValueError("Target column not in dataframe")
X = df.drop(columns=[target])
y = df[target]
# For classification, check if y needs encoding
if y.dtype == 'object' or y.dtype.name == 'category':
le = LabelEncoder()
y = le.fit_transform(y)
return X, y
def auto_build_preprocessor(X: pd.DataFrame, scaler_choice: str="standard", onehot=True):
num_cols = X.select_dtypes(include=[np.number]).columns.tolist()
cat_cols = X.select_dtypes(include=["object", "category"]).columns.tolist()
transformers = []
if num_cols:
if scaler_choice == "standard":
num_pipeline = Pipeline([("imputer", SimpleImputer(strategy="mean")), ("scaler", StandardScaler())])
elif scaler_choice == "minmax":
num_pipeline = Pipeline([("imputer", SimpleImputer(strategy="mean")), ("scaler", MinMaxScaler())])
else:
num_pipeline = Pipeline([("imputer", SimpleImputer(strategy="mean"))])
transformers.append(("num", num_pipeline, num_cols))
if cat_cols and onehot:
cat_pipeline = Pipeline([("imputer", SimpleImputer(strategy="most_frequent")),
("onehot", OneHotEncoder(drop='first', sparse=False, handle_unknown='ignore'))])
transformers.append(("cat", cat_pipeline, cat_cols))
preprocessor = ColumnTransformer(transformers=transformers, remainder='drop')
return preprocessor
def run_regression_classification(task: str, model_name: str, X, y, scaler_choice="standard",
test_size=0.2, random_state=42, do_rfecv=False,
do_hyperparameter_tuning=False):
if task == "regression":
ModelClass = REGRESSION_MODELS.get(model_name)
else:
ModelClass = CLASSIFICATION_MODELS.get(model_name)
if ModelClass is None:
raise ValueError(f"Model {model_name} not found for task {task}")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
preprocessor = auto_build_preprocessor(X_train, scaler_choice=scaler_choice, onehot=True)
model = ModelClass()
# Create pipeline
pipe = Pipeline([("pre", preprocessor), ("model", model)])
# Hyperparameter tuning if requested
if do_hyperparameter_tuning and model_name in MODEL_PARAM_GRIDS:
param_grid = MODEL_PARAM_GRIDS[model_name]
gs = GridSearchCV(pipe, param_grid, cv=3, n_jobs=1, scoring='r2' if task=='regression' else 'accuracy')
gs.fit(X_train, y_train)
trained = gs.best_estimator_
y_pred = trained.predict(X_test)
best_params = gs.best_params_
else:
pipe.fit(X_train, y_train)
trained = pipe
y_pred = pipe.predict(X_test)
best_params = None
results = {}
# Calculate metrics
if task == "regression":
results["mse"] = mean_squared_error(y_test, y_pred)
results["rmse"] = math.sqrt(results["mse"])
results["mae"] = mean_absolute_error(y_test, y_pred)
results["r2"] = r2_score(y_test, y_pred)
# Cross-validation scores
cv_scores = cross_val_score(pipe, X, y, cv=5, scoring='r2')
results["cv_r2_mean"] = cv_scores.mean()
results["cv_r2_std"] = cv_scores.std()
# Residuals
resid = y_test - y_pred
results["residuals"] = resid
else: # classification
results["accuracy"] = accuracy_score(y_test, y_pred)
results["report"] = classification_report(y_test, y_pred, output_dict=True)
results["confusion_matrix"] = confusion_matrix(y_test, y_pred).tolist()
# Cross-validation scores
cv_scores = cross_val_score(pipe, X, y, cv=5, scoring='accuracy')
results["cv_accuracy_mean"] = cv_scores.mean()
results["cv_accuracy_std"] = cv_scores.std()
# ROC AUC if applicable
try:
if hasattr(trained.named_steps['model'], "predict_proba"):
y_score = trained.predict_proba(X_test)[:,1]
results["roc_auc"] = roc_auc_score(y_test, y_score)
fpr, tpr, _ = roc_curve(y_test, y_score)
results["roc_curve"] = (fpr.tolist(), tpr.tolist())
except:
pass
# Feature importance
feat_importance = None
try:
model_obj = trained.named_steps['model']
if hasattr(model_obj, "feature_importances_"):
feature_names = []
pre = trained.named_steps['pre']
if hasattr(pre, 'transformers_'):
for name, trans, cols in pre.transformers_:
if name == "num":
feature_names += cols
elif name == "cat":
ohe = trans.named_steps['onehot']
if hasattr(ohe, 'get_feature_names_out'):
names = list(ohe.get_feature_names_out(cols))
feature_names += names
else:
feature_names += cols
importances = model_obj.feature_importances_
feat_importance = list(zip(feature_names, importances))
feat_importance.sort(key=lambda x: x[1], reverse=True)
except:
pass
results["feature_importance"] = feat_importance
results["trained_model"] = trained
results["best_params"] = best_params
results["y_test"] = y_test
results["y_pred"] = y_pred
return results
# -----------------------------
# Clustering Functions
# -----------------------------
def run_clustering(clustering_method: str, X, n_clusters=3, eps=0.5, min_samples=5):
X_scaled = StandardScaler().fit_transform(X.select_dtypes(include=[np.number]))
if clustering_method == "K-Means":
model = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
labels = model.fit_predict(X_scaled)
centroids = model.cluster_centers_
# Calculate metrics
try:
silhouette = silhouette_score(X_scaled, labels)
davies_bouldin = davies_bouldin_score(X_scaled, labels)
calinski_harabasz = calinski_harabasz_score(X_scaled, labels)
except:
silhouette = None
davies_bouldin = None
calinski_harabasz = None
results = {
"method": "K-Means",
"labels": labels,
"centroids": centroids,
"inertia": model.inertia_,
"silhouette_score": silhouette,
"davies_bouldin_score": davies_bouldin,
"calinski_harabasz_score": calinski_harabasz,
"model": model
}
elif clustering_method == "DBSCAN":
model = DBSCAN(eps=eps, min_samples=min_samples)
labels = model.fit_predict(X_scaled)
# Calculate metrics (only if more than 1 cluster found)
n_clusters_found = len(set(labels)) - (1 if -1 in labels else 0)
if n_clusters_found > 1:
try:
silhouette = silhouette_score(X_scaled, labels)
davies_bouldin = davies_bouldin_score(X_scaled, labels)
calinski_harabasz = calinski_harabasz_score(X_scaled, labels)
except:
silhouette = None
davies_bouldin = None
calinski_harabasz = None
else:
silhouette = None
davies_bouldin = None
calinski_harabasz = None
results = {
"method": "DBSCAN",
"labels": labels,
"core_sample_indices": model.core_sample_indices_,
"n_clusters_found": n_clusters_found,
"silhouette_score": silhouette,
"davies_bouldin_score": davies_bouldin,
"calinski_harabasz_score": calinski_harabasz,
"model": model
}
return results
# -----------------------------
# Rule-Based (Apriori) Functions
# -----------------------------
def prepare_transaction_data(df, transaction_col=None, threshold=0.5):
"""
Prepare data for association rule mining.
For categorical columns, create binary indicators.
For numeric columns, bin them into categories.
"""
df_prep = df.copy()
# Convert numeric columns to categorical by binning
for col in df_prep.select_dtypes(include=[np.number]).columns:
if len(df_prep[col].unique()) > 10: # Only bin if many unique values
# Create 5 equal-width bins
df_prep[col] = pd.qcut(df_prep[col], q=5, duplicates='drop')
df_prep[col] = df_prep[col].astype(str)
# Convert all to string for consistency
for col in df_prep.columns:
df_prep[col] = df_prep[col].astype(str)
# If transaction column is specified, use it
if transaction_col and transaction_col in df_prep.columns:
transactions = []
for items in df_prep[transaction_col]:
if pd.isna(items):
transactions.append([])
else:
# Split by common delimiters
if isinstance(items, str):
split_items = [item.strip() for item in str(items).replace(';', ',').split(',')]
transactions.append([f"{transaction_col}={item}" for item in split_items if item])
else:
transactions.append([f"{transaction_col}={items}"])
else:
# Create transactions from entire dataframe (one-hot like)
transactions = []
for _, row in df_prep.iterrows():
transaction = []
for col in df_prep.columns:
if col == transaction_col:
continue
value = str(row[col])
if value and value.lower() not in ['nan', 'null', 'none', '']:
transaction.append(f"{col}={value}")
if transaction:
transactions.append(transaction)
return transactions
def simple_apriori(transactions, min_support=0.1):
"""
A simplified Apriori algorithm implementation.
Returns frequent itemsets and their support.
"""
from collections import defaultdict
import itertools
# Count item frequencies
item_counts = defaultdict(int)
for transaction in transactions:
for item in set(transaction): # Use set to avoid duplicate items in same transaction
item_counts[item] += 1
total_transactions = len(transactions)
# Get frequent 1-itemsets
frequent_1_itemsets = {}
for item, count in item_counts.items():
support = count / total_transactions
if support >= min_support:
frequent_1_itemsets[frozenset([item])] = support
frequent_itemsets = {1: frequent_1_itemsets}
# Generate larger itemsets (up to 3-itemsets for simplicity)
k = 2
while True:
candidate_itemsets = set()
prev_itemsets = list(frequent_itemsets[k-1].keys())
# Generate candidates
for i in range(len(prev_itemsets)):
for j in range(i+1, len(prev_itemsets)):
itemset1 = prev_itemsets[i]
itemset2 = prev_itemsets[j]
union_set = itemset1.union(itemset2)
if len(union_set) == k:
candidate_itemsets.add(union_set)
if not candidate_itemsets:
break
# Count support for candidates
candidate_counts = defaultdict(int)
for transaction in transactions:
trans_set = set(transaction)
for candidate in candidate_itemsets:
if candidate.issubset(trans_set):
candidate_counts[candidate] += 1
# Filter by minimum support
frequent_k_itemsets = {}
for itemset, count in candidate_counts.items():
support = count / total_transactions
if support >= min_support:
frequent_k_itemsets[itemset] = support
if not frequent_k_itemsets:
break
frequent_itemsets[k] = frequent_k_itemsets
k += 1
if k > 3: # Limit to 3-itemsets for performance
break
return frequent_itemsets
def generate_association_rules(frequent_itemsets, min_confidence=0.5, min_lift=1.0):
"""Generate association rules from frequent itemsets."""
rules = []
for k, itemsets in frequent_itemsets.items():
if k < 2:
continue
for itemset, support_itemset in itemsets.items():
itemset_list = list(itemset)
# Generate all non-empty proper subsets
for i in range(1, k):
for antecedent in itertools.combinations(itemset_list, i):
antecedent_set = frozenset(antecedent)
consequent_set = itemset - antecedent_set
if not consequent_set:
continue
# Find support of antecedent
antecedent_support = None
for size in range(1, k):
if size in frequent_itemsets and antecedent_set in frequent_itemsets[size]:
antecedent_support = frequent_itemsets[size][antecedent_set]
break
if antecedent_support is None or antecedent_support == 0:
continue
# Calculate confidence and lift
confidence = support_itemset / antecedent_support
consequent_support = None
# Find support of consequent
for size in range(1, k):
if size in frequent_itemsets and consequent_set in frequent_itemsets[size]:
consequent_support = frequent_itemsets[size][consequent_set]
break
if consequent_support is None or consequent_support == 0:
continue
lift = confidence / consequent_support
if confidence >= min_confidence and lift >= min_lift:
rules.append({
'antecedents': set(antecedent),
'consequents': set(consequent_set),
'support': support_itemset,
'confidence': confidence,
'lift': lift
})
# Sort by confidence, then lift
rules.sort(key=lambda x: (x['confidence'], x['lift']), reverse=True)
return rules
def run_association_mining(transactions, min_support=0.1, min_confidence=0.5, min_lift=1.0):
"""Run association rule mining using our simple implementation."""
frequent_itemsets = simple_apriori(transactions, min_support)
rules = generate_association_rules(frequent_itemsets, min_confidence, min_lift)
return frequent_itemsets, rules
def run_frequency_based_rules(df, categorical_cols=None, min_frequency=0.1, min_cooccurrence=0.5):
"""
An alternative rule discovery method based on frequency and co-occurrence.
Simpler but effective for many use cases.
"""
if categorical_cols is None:
categorical_cols = df.select_dtypes(include=['object', 'category']).columns.tolist()
rules = []
for col1 in categorical_cols:
for col2 in categorical_cols:
if col1 == col2:
continue
# Calculate contingency table
contingency = pd.crosstab(df[col1], df[col2], normalize='all')
# Find strong associations
for val1 in contingency.index:
for val2 in contingency.columns:
p_val1_val2 = contingency.loc[val1, val2]
p_val1 = df[col1].value_counts(normalize=True).get(val1, 0)
p_val2 = df[col2].value_counts(normalize=True).get(val2, 0)
if p_val1 > 0 and p_val2 > 0 and p_val1_val2 > 0:
confidence = p_val1_val2 / p_val1
lift = p_val1_val2 / (p_val1 * p_val2)
if p_val1_val2 >= min_frequency and confidence >= min_cooccurrence:
rules.append({
'rule': f"If {col1} = {val1} then {col2} = {val2}",
'support': p_val1_val2,
'confidence': confidence,
'lift': lift,
'antecedent': f"{col1}={val1}",
'consequent': f"{col2}={val2}"
})
# Sort by confidence and lift
rules.sort(key=lambda x: (x['confidence'], x['lift']), reverse=True)
return rules
def run_apriori(df_encoded, min_support=0.1, min_confidence=0.5, min_lift=1.0, max_length=4):
# Find frequent itemsets
frequent_itemsets = apriori(df_encoded, min_support=min_support, use_colnames=True, max_len=max_length)
if frequent_itemsets.empty:
return None, None
# Generate association rules
rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=min_confidence)
rules = rules[rules['lift'] >= min_lift]
# Sort by confidence and lift
rules = rules.sort_values(['confidence', 'lift'], ascending=[False, False])
return frequent_itemsets, rules
def run_fp_growth(df_encoded, min_support=0.1, min_confidence=0.5, min_lift=1.0, max_length=4):
# Find frequent itemsets using FP-Growth
frequent_itemsets = fpgrowth(df_encoded, min_support=min_support, use_colnames=True, max_len=max_length)
if frequent_itemsets.empty:
return None, None
# Generate association rules
rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=min_confidence)
rules = rules[rules['lift'] >= min_lift]
# Sort by confidence and lift
rules = rules.sort_values(['confidence', 'lift'], ascending=[False, False])
return frequent_itemsets, rules
# -----------------------------
# Gradio UI with Enhanced Model Tab
# -----------------------------
css = """
/* Vanta background container */
#vanta-bg {
width: 100%;
height: 380px;
position: relative;
overflow: hidden;
border-radius: 12px;
margin-bottom: 8px;
}
.login-card {
position: absolute;
left: 50%;
top: 50%;
transform: translate(-50%, -50%);
width: 420px;
max-width: calc(100% - 24px);
background: linear-gradient(180deg, rgba(255,255,255,0.98), rgba(245,245,255,0.95));
border-radius: 14px;
box-shadow: 0 12px 36px rgba(0,0,0,0.18);
padding: 22px;
z-index: 999;
border: 1px solid rgba(0,0,0,0.06);
}
.login-logo {
display:flex;
align-items:center;
gap:12px;
margin-bottom:8px;
}
.brand-title {
font-weight:700;
font-size:18px;
color:#2b2b6b;
}
.btn-animate {
transition: transform 0.12s ease-in-out, box-shadow 0.12s;
}
.btn-animate:active {
transform: translateY(2px) scale(0.995);
box-shadow: 0 6px 18px rgba(0,0,0,0.12) inset;
}
.app-desc {
font-size: 13px;
color: #444;
margin-top: 8px;
text-align: center;
}
#profile-report table {
width: 100%;
border-collapse: collapse;
font-size: 14px;
table-layout: fixed;
}
#profile-report th, #profile-report td {
padding: 8px 12px;
border: 1px solid #ddd;
text-align: left;
word-wrap: break-word;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
}
#profile-report th {
background-color: #f5f5f5;
font-weight: 600;
}
#profile-report tr:nth-child(even) {
background-color: #f9f9f9;
}
.markdown-body {
max-width: 100% !important;
}
.gr-markdown {
max-height: 70vh;
overflow-y: auto;
}
.dataframe-container {
max-height: 500px;
overflow-y: auto;
border: 1px solid #ddd;
border-radius: 8px;
padding: 10px;
}
.model-section {
padding: 15px;
border: 1px solid #e0e0e0;
border-radius: 8px;
margin-bottom: 15px;
background-color: #f9f9f9;
}
.model-metrics {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 10px;
margin: 15px 0;
}
.metric-card {
background: white;
padding: 12px;
border-radius: 6px;
border-left: 4px solid #2b2b6b;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
.metric-value {
font-size: 24px;
font-weight: bold;
color: #2b2b6b;
}
.metric-label {
font-size: 12px;
color: #666;
text-transform: uppercase;
}
"""
vanta_html = """
<div id="vanta-bg" style="width:100%;height:380px;border-radius:12px;position:relative;">
<div class="login-card" role="region" aria-label="Login card">
<div class="login-logo">
<img src="{LOGO}" alt="logo" style="height:48px;width:48px;border-radius:8px;"/>
<div>
<div class="brand-title">DataSynth — Analytics Hub</div>
<div style="font-size:12px;color:#666;">Fast, modular data profiling & model building</div>
</div>
</div>
<div style="margin-top:8px;">
<div style="font-size:13px;color:#333;margin-bottom:6px;">Sign in to continue</div>
</div>
<div class="app-desc">Upload your dataset, prepare it, visualize with NL, and build ML models — all in one place.</div>
</div>
</div>
<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r121/three.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/vanta@0.5.21/dist/vanta.net.min.js"></script>
<script>
(function(){
try {
if (typeof VANTA !== 'undefined') {
VANTA.NET({
el: "#vanta-bg",
color: 0x2b2b6b,
backgroundColor: 0xffffff,
points: 10.00,
maxDistance: 26.00
});
}
} catch(e) {
console.warn("Vanta failed to initialize", e);
}
})();
</script>
"""
vanta_html = vanta_html.replace("{LOGO}", LOGO_PATH)
with gr.Blocks(css=css, title="DataSynth — Analytics Hub") as demo:
with gr.Column():
v_html = gr.HTML(vanta_html)
with gr.Row():
username_in = gr.Textbox(label="Username", placeholder="username", interactive=True)
password_in = gr.Textbox(label="Password", placeholder="password", type="password")
with gr.Row():
login_btn = gr.Button("Log in", elem_classes="btn-animate")
login_msg = gr.Label(value="")
raw_state = gr.State(value=None)
df_state = gr.State(value=None)
clean_state = gr.State(value=None)
profile_state = gr.State(value=None)
model_results_state = gr.State(value=None)
with gr.Column(visible=False) as main_area:
gr.Markdown("# 🚀 DataSynth Analytics Workspace")
with gr.Tabs() as main_tabs:
with gr.TabItem("📊 Data Profiling"):
gr.Markdown("### 📁 Upload & Analyze Your Dataset")
upload = gr.File(label="Upload Dataset", file_types=[".csv", ".xlsx", ".xls", ".xlsm", ".xlsb", ".json", ".parquet", ".feather", ".txt", ".tsv"],
type="filepath")
with gr.Row():
profile_btn = gr.Button("🚀 Run Comprehensive Data Profiling", variant="primary", size="lg")
with gr.Tabs() as data_tabs:
with gr.TabItem("📊 Profile Report"):
profile_md = gr.Markdown(
"No dataset loaded. Upload your data and click 'Run Comprehensive Data Profiling'.",
elem_id="profile-report"
)
with gr.TabItem("👁️ Data Preview"):
gr.HTML("<div class='dataframe-container'>")
sample_table = gr.Dataframe(
interactive=False,
label="Sample Data (First 100 rows)",
)
gr.HTML("</div>")
with gr.TabItem("📈 Visualizations"):
gr.Markdown("### Correlation Analysis")
corr_plot = gr.Plot(label="Correlation Heatmap")
with gr.TabItem("🔧 Prepare"):
gr.Markdown("## 🛠️ Advanced Data Preparation Studio")
with gr.Tabs() as prep_tabs:
with gr.TabItem("📋 Column Management"):
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Column Selection & Operations")
available_cols = gr.CheckboxGroup(
label="Select Columns to Keep",
choices=[],
interactive=True
)
select_all_cols = gr.Button("Select All")
deselect_all_cols = gr.Button("Deselect All")
gr.Markdown("### Date Operations")
date_col_selected = gr.Dropdown(
label="Date Column",
choices=[],
interactive=True
)
# Add other missing components as needed:
text_ops_cols = gr.CheckboxGroup(
label="Select Text Columns",
choices=[],
interactive=True
)
groupby_cols = gr.Dropdown(
label="Group By Columns",
choices=[],
interactive=True,
multiselect=True
)
agg_columns = gr.Dropdown(
label="Columns to Aggregate",
choices=[],
interactive=True,
multiselect=True
)
pivot_columns = gr.Dropdown(
label="Columns to Pivot",
choices=[],
interactive=True,
multiselect=True
)
pivot_values = gr.Dropdown(
label="Values Columns",
choices=[],
interactive=True,
multiselect=True
)
join_key = gr.Dropdown(
label="Join Key Column",
choices=[],
interactive=True
)
gr.Markdown("### Column Renaming")
rename_col_old = gr.Dropdown(
label="Column to Rename",
choices=[],
interactive=True
)
rename_col_new = gr.Textbox(
label="New Column Name",
placeholder="Enter new column name"
)
rename_btn = gr.Button("Rename Column")
gr.Markdown("### Data Type Conversion")
dtype_col = gr.Dropdown(
label="Column to Convert",
choices=[],
interactive=True
)
dtype_target = gr.Dropdown(
label="Target Data Type",
choices=["string", "numeric", "integer", "float", "datetime", "category", "boolean"],
value="string",
interactive=True
)
convert_dtype_btn = gr.Button("Convert Data Type")
with gr.Column(scale=1):
gr.Markdown("### High Cardinality Management")
high_card_threshold = gr.Slider(
minimum=1,
maximum=100,
value=50,
step=1,
label="High Cardinality Threshold (unique values)"
)
high_card_action = gr.Radio(
choices=["Show only", "Drop columns"],
value="Show only",
label="Action for High Cardinality Columns"
)
high_card_btn = gr.Button("Apply High Cardinality Filter")
high_card_results = gr.Markdown("High cardinality columns will appear here")
with gr.TabItem("🎯 Missing Values"):
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Numeric Imputation")
num_impute_cols = gr.CheckboxGroup(
label="Select Numeric Columns",
choices=[],
interactive=True
)
num_impute_method = gr.Dropdown(
choices=[
"Mean", "Median", "Mode", "KNN", "MICE",
"LOCF", "NOCB", "Linear Interpolation",
"Forward Fill", "Backward Fill", "Zero", "Custom Value"
],
value="Median",
label="Imputation Method"
)
num_custom_value = gr.Textbox(
label="Custom Value (if selected)",
visible=False
)
gr.Markdown("### Categorical Imputation")
cat_impute_cols = gr.CheckboxGroup(
label="Select Categorical Columns",
choices=[],
interactive=True
)
cat_impute_method = gr.Dropdown(
choices=[
"Mode", "Most Frequent", "Arbitrary ('Unknown')",
"KNN", "MICE", "Constant Value"
],
value="Mode",
label="Imputation Method"
)
cat_custom_value = gr.Textbox(
label="Custom Value (if selected)",
value="Unknown",
visible=False
)
impute_btn = gr.Button("Apply Imputation")
with gr.Column(scale=1):
gr.Markdown("### Missing Value Analysis")
missing_summary = gr.Markdown("Missing value summary will appear here")
missing_heatmap = gr.Plot(label="Missing Value Heatmap")
with gr.TabItem("⚙️ Feature Engineering"):
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Text Operations")
text_ops_cols = gr.CheckboxGroup(
label="Select Text Columns",
choices=[],
interactive=True
)
text_operations = gr.CheckboxGroup(
choices=[
"Remove leading/trailing whitespace",
"Convert to lowercase",
"Convert to uppercase",
"Remove special characters",
"Remove numbers",
"Remove extra spaces"
],
label="Text Operations"
)
gr.Markdown("### Value Replacement")
replace_col = gr.Dropdown(
label="Column for Value Replacement",
choices=[],
interactive=True
)
replace_old = gr.Textbox(
label="Value to Replace",
placeholder="Enter value to find"
)
replace_new = gr.Textbox(
label="Replacement Value",
placeholder="Enter new value"
)
replace_btn = gr.Button("Replace Values")
gr.Markdown("### Text to Columns")
split_col = gr.Dropdown(
label="Column to Split",
choices=[],
interactive=True
)
split_delimiter = gr.Textbox(
label="Delimiter",
value=",",
placeholder="Enter delimiter (e.g., ',', ';', ' ')",
max_lines=1
)
split_btn = gr.Button("Split Column")
with gr.Column(scale=1):
gr.Markdown("### Formula-Based Columns")
formula_expr = gr.Textbox(
label="Formula Expression",
placeholder="Example: Sales * Quantity, or LEFT(ProductName, 3)",
lines=2
)
formula_new_col = gr.Textbox(
label="New Column Name",
placeholder="Enter name for new column"
)
formula_examples = gr.Markdown("""
**Formula Examples:**
- `Revenue = Sales * Price`
- `FullName = FirstName + ' ' + LastName`
- `Profit = Revenue - Cost`
- `Category = LEFT(ProductCode, 3)`
- `Age = CURRENT_YEAR - BirthYear`
""")
formula_btn = gr.Button("Create New Column")
with gr.Row():
apply_all_btn = gr.Button("🚀 Apply All Changes", variant="primary", size="lg")
download_clean_btn = gr.Button("📥 Download Clean CSV", variant="secondary")
prep_output = gr.Markdown("Preparation status will appear here")
with gr.TabItem("📊 Visualize (NL)"):
gr.Markdown("Type a natural-language request to create a plot (e.g., 'histogram of age', 'scatter income vs age', 'bar of country').")
nl_input = gr.Textbox(label="Describe chart")
nl_btn = gr.Button("Create Chart")
nl_plot = gr.Plot()
with gr.TabItem("🤖 Model"):
gr.Markdown("## 🤖 Machine Learning Studio")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### 🎯 Select Task Type")
task_select = gr.Radio(
choices=["Regression", "Classification", "Clustering", "Rule-Based (Association)"],
value="Regression",
label="ML Task Type"
)
gr.Markdown("### 📊 Dataset Selection")
dataset_select = gr.Radio(
choices=["Use Original Data", "Use Cleaned/Transformed Data"],
value="Use Original Data",
label="Select Dataset"
)
# Common for Regression/Classification
with gr.Column(visible=True) as reg_class_params:
gr.Markdown("### 📈 Regression/Classification Settings")
target_col = gr.Dropdown(
choices=[],
label="Target Variable",
interactive=True
)
scaler_choice = gr.Dropdown(
choices=["StandardScaler", "MinMaxScaler", "None"],
value="StandardScaler",
label="Feature Scaling"
)
model_select = gr.Dropdown(
choices=list(REGRESSION_MODELS.keys()),
value="Random Forest Regressor",
label="Select Model"
)
rfecv_opt = gr.Checkbox(
label="Run RFECV (Feature Selection)",
value=False
)
hyperparam_tuning = gr.Checkbox(
label="Hyperparameter Tuning",
value=False
)
# Clustering specific
with gr.Column(visible=False) as clustering_params:
gr.Markdown("### 🎪 Clustering Settings")
clustering_method = gr.Radio(
choices=["K-Means", "DBSCAN"],
value="K-Means",
label="Clustering Algorithm"
)
with gr.Row():
n_clusters = gr.Slider(
minimum=2,
maximum=20,
value=3,
step=1,
label="Number of Clusters (K-Means)"
)
with gr.Row(visible=False) as dbscan_params:
eps_value = gr.Slider(
minimum=0.1,
maximum=5.0,
value=0.5,
step=0.1,
label="Epsilon (DBSCAN)"
)
min_samples_value = gr.Slider(
minimum=2,
maximum=20,
value=5,
step=1,
label="Min Samples (DBSCAN)"
)
# Rule-Based specific
with gr.Column(visible=False) as rule_based_params:
gr.Markdown("### 🔗 Rule-Based Mining Settings")
rule_method = gr.Radio(
choices=["Apriori (Simple)", "Frequency-Based Rules"],
value="Apriori (Simple)",
label="Rule Discovery Method"
)
transaction_col = gr.Dropdown(
choices=[],
label="Transaction Column (optional - for Apriori)",
interactive=True
)
with gr.Row():
min_support = gr.Slider(
minimum=0.01,
maximum=1.0,
value=0.1,
step=0.01,
label="Minimum Support"
)
min_confidence = gr.Slider(
minimum=0.01,
maximum=1.0,
value=0.5,
step=0.01,
label="Minimum Confidence"
)
with gr.Row():
min_lift = gr.Slider(
minimum=0.1,
maximum=10.0,
value=1.0,
step=0.1,
label="Minimum Lift"
)
max_rule_length = gr.Slider(
minimum=2,
maximum=10,
value=4,
step=1,
label="Maximum Rule Length"
)
with gr.Column(scale=2):
gr.Markdown("### 🚀 Model Training & Evaluation")
train_btn = gr.Button("Train Model", variant="primary", size="lg")
# Results display
model_results = gr.Markdown("Model results will appear here...")
# Performance metrics display
with gr.Column(visible=False) as perf_metrics:
gr.Markdown("### 📊 Performance Metrics")
metrics_display = gr.HTML("")
# Feature importance/Cluster visualization
model_plot = gr.Plot(label="Visualization")
# Detailed results
with gr.Tabs():
with gr.TabItem("📋 Detailed Results"):
detailed_results = gr.Dataframe(
label="Detailed Results",
interactive=False,
wrap=True
)
with gr.TabItem("📈 Performance Graphs"):
performance_plots = gr.Plot(label="Performance Analysis")
with gr.TabItem("💾 Export Results"):
export_results = gr.File(label="Download Results")
export_btn = gr.Button("Generate Export")
# Model comparison section
with gr.Row(visible=False) as model_comparison:
gr.Markdown("## 🏆 Top 5 Models Comparison")
compare_results = gr.Dataframe(label="Model Comparison")
compare_plot = gr.Plot(label="Comparison Visualization")
# Model improvement section
with gr.Row(visible=False) as model_improvement:
gr.Markdown("## 🛠️ Improve Model Performance")
with gr.Column():
improvement_method = gr.Radio(
choices=["Hyperparameter Tuning", "Feature Engineering", "Ensemble Methods", "Cross-Validation"],
value="Hyperparameter Tuning",
label="Improvement Method"
)
with gr.Column(visible=True) as hyperparam_tuning_options:
gr.Markdown("### Hyperparameter Tuning Options")
tuning_method = gr.Radio(
choices=["Grid Search", "Random Search", "Bayesian Optimization"],
value="Grid Search",
label="Tuning Method"
)
cv_folds = gr.Slider(
minimum=3,
maximum=10,
value=5,
step=1,
label="CV Folds"
)
improve_btn = gr.Button("Apply Improvement", variant="primary")
improvement_results = gr.Markdown("Improvement results will appear here...")
with gr.Column():
gr.Markdown("### Model Interpretation")
interpretability_method = gr.Radio(
choices=["Feature Importance", "SHAP Values", "Partial Dependence Plots", "LIME"],
value="Feature Importance",
label="Interpretability Method"
)
interpret_btn = gr.Button("Generate Interpretation")
interpretation_plot = gr.Plot(label="Model Interpretation")
with gr.TabItem("📄 Report"):
gr.Markdown("Generate a comprehensive report summarizing all analyses.")
report_btn = gr.Button("Generate Comprehensive Report")
report_download = gr.File(label="Download Report (.md)")
# --- Callbacks ---
def _do_login(username, password):
if not username or not password:
return gr.update(value="Enter username and password"), gr.update(visible=False)
ok = authenticate(username.strip(), password.strip())
if ok:
return gr.update(value=f"Welcome — {username}"), gr.update(visible=True)
else:
return gr.update(value="Invalid credentials"), gr.update(visible=False)
login_btn.click(fn=_do_login, inputs=[username_in, password_in], outputs=[login_msg, main_area])
available_cols = gr.CheckboxGroup(choices=[], interactive=True, label="Select Columns to Keep")
rename_col_old = gr.Dropdown(choices=[], interactive=True, label="Column to Rename")
dtype_col = gr.Dropdown(choices=[], interactive=True, label="Column to Convert")
num_impute_cols = gr.CheckboxGroup(choices=[], interactive=True, label="Select Numeric Columns")
cat_impute_cols = gr.CheckboxGroup(choices=[], interactive=True, label="Select Categorical Columns")
replace_col = gr.Dropdown(choices=[], interactive=True, label="Column for Value Replacement")
split_col = gr.Dropdown(choices=[], interactive=True, label="Column to Split")
date_col_selected = gr.Dropdown(choices=[], interactive=True, label="Date Column")
pivot_index = gr.Dropdown(choices=[], interactive=True, label="Index Columns", multiselect=True)
text_ops_cols = gr.CheckboxGroup(choices=[], interactive=True, label="Select Text Columns")
groupby_cols = gr.Dropdown(choices=[], interactive=True, label="Group By Columns", multiselect=True)
agg_columns = gr.Dropdown(choices=[], interactive=True, label="Columns to Aggregate", multiselect=True)
pivot_columns = gr.Dropdown(choices=[], interactive=True, label="Columns to Pivot", multiselect=True)
pivot_values = gr.Dropdown(choices=[], interactive=True, label="Values Columns", multiselect=True)
join_key = gr.Dropdown(choices=[], interactive=True, label="Join Key Column")
prepare_widget_outputs = [
available_cols, rename_col_old, dtype_col, num_impute_cols,
cat_impute_cols, replace_col, split_col, date_col_selected, pivot_index,
text_ops_cols, groupby_cols, agg_columns, pivot_columns, pivot_values, join_key
]
# Load & profile dataset
def _load_and_profile(uploaded):
try:
if uploaded is None:
base_fail = (gr.update(value="No file uploaded."),
pd.DataFrame(), None, None, None)
return base_fail + tuple([gr.update()]*len(prepare_widget_outputs))
df = read_file_to_df(uploaded)
prof = comprehensive_data_profile(df)
md = profile_to_enhanced_markdown(prof)
corr_plot_fig = create_correlation_plot(df, prof)
tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
df.to_csv(tmp.name, index=False)
prepare_updates = update_column_lists(df)
if prepare_updates is None:
prepare_updates = ()
elif not isinstance(prepare_updates, (list, tuple)):
prepare_updates = (prepare_updates,)
else:
prepare_updates = tuple(prepare_updates)
needed = len(prepare_widget_outputs)
if len(prepare_updates) < needed:
prepare_updates = prepare_updates + tuple([gr.update()] * (needed - len(prepare_updates)))
elif len(prepare_updates) > needed:
prepare_updates = prepare_updates[:needed]
return (md, df.head(100), tmp.name, prof, corr_plot_fig, *prepare_updates)
except Exception as e:
traceback.print_exc()
base_fail = (gr.update(value=f"Error loading file: {e}"),
pd.DataFrame(), None, None, None)
return base_fail + tuple([gr.update()] * len(prepare_widget_outputs))
profile_btn.click(
fn=_load_and_profile,
inputs=[upload],
outputs=[
profile_md, sample_table, df_state, profile_state, corr_plot,
*prepare_widget_outputs
]
)
# Update task-specific UI based on task selection
def update_task_ui(task_type):
if task_type == "Regression":
return (
gr.Column.update(visible=True), # reg_class_params
gr.Column.update(visible=False), # clustering_params
gr.Column.update(visible=False), # rule_based_params
gr.Dropdown.update(choices=list(REGRESSION_MODELS.keys()), value="Random Forest Regressor"), # model_select
gr.Row.update(visible=False), # model_comparison
gr.Row.update(visible=False) # model_improvement
)
elif task_type == "Classification":
return (
gr.Column.update(visible=True),
gr.Column.update(visible=False),
gr.Column.update(visible=False),
gr.Dropdown.update(choices=list(CLASSIFICATION_MODELS.keys()), value="Random Forest Classifier"),
gr.Row.update(visible=False),
gr.Row.update(visible=False)
)
elif task_type == "Clustering":
return (
gr.Column.update(visible=False),
gr.Column.update(visible=True),
gr.Column.update(visible=False),
gr.Dropdown.update(choices=[], value=None),
gr.Row.update(visible=True),
gr.Row.update(visible=True)
)
else: # Rule-Based
return (
gr.Column.update(visible=False),
gr.Column.update(visible=False),
gr.Column.update(visible=True),
gr.Dropdown.update(choices=[], value=None),
gr.Row.update(visible=False),
gr.Row.update(visible=False)
)
task_select.change(
fn=update_task_ui,
inputs=[task_select],
outputs=[
reg_class_params, clustering_params, rule_based_params,
model_select, model_comparison, model_improvement
]
)
# Update DBSCAN parameters visibility
def update_clustering_ui(clustering_method):
if clustering_method == "DBSCAN":
return gr.Row.update(visible=True)
else:
return gr.Row.update(visible=False)
clustering_method.change(
fn=update_clustering_ui,
inputs=[clustering_method],
outputs=[dbscan_params]
)
# Train model based on task type
def train_model_wrapper(task_type, model_name, dataset_choice, target_var,
scaler_type, do_rfecv, do_tuning,
clustering_algo, n_clusters, eps, min_samples,
rule_algo, trans_col, min_sup, min_conf, min_lft, max_len,
df_path, clean_path):
try:
# Select dataset
if dataset_choice == "Use Cleaned/Transformed Data" and clean_path:
df = pd.read_csv(clean_path)
elif df_path:
df = pd.read_csv(df_path)
else:
return "No dataset available.", None, None, None
results_summary = ""
plot_fig = None
detailed_df = pd.DataFrame()
if task_type in ["Regression", "Classification"]:
if not target_var or target_var not in df.columns:
return "Please select a valid target variable.", None, None, None
X, y = prepare_features_targets(df, target_var)
# Train multiple models for comparison
if task_type == "Regression":
model_list = ["Random Forest Regressor", "Gradient Boosting Regressor",
"Linear Regression", "Ridge Regression", "Decision Tree Regressor"]
else:
model_list = ["Random Forest Classifier", "Gradient Boosting Classifier",
"Logistic Regression", "Decision Tree Classifier", "Gaussian Naive Bayes"]
all_results = []
comparison_data = []
for model in model_list[:5]: # Top 5 models
try:
task_lower = task_type.lower()
model_results = run_regression_classification(
task=task_lower,
model_name=model,
X=X,
y=y,
scaler_choice=scaler_type,
do_rfecv=do_rfecv,
do_hyperparameter_tuning=do_tuning
)
if task_type == "Regression":
score = model_results.get("r2", 0)
else:
score = model_results.get("accuracy", 0)
comparison_data.append({
"Model": model,
"Score": round(score, 4),
"Best Params": str(model_results.get("best_params", "Default"))
})
all_results.append((model, model_results))
except Exception as e:
print(f"Error training {model}: {e}")
continue
# Select best model
if all_results:
best_model, best_results = max(all_results, key=lambda x:
x[1]["r2"] if task_type=="Regression" else x[1]["accuracy"])
# Generate summary
results_summary = f"## 🏆 Best Model: {best_model}\n\n"
if task_type == "Regression":
results_summary += f"""
### 📊 Regression Metrics
- **R² Score:** {best_results['r2']:.4f}
- **RMSE:** {best_results['rmse']:.4f}
- **MAE:** {best_results['mae']:.4f}
- **Cross-Validation R²:** {best_results.get('cv_r2_mean', 0):.4f} (±{best_results.get('cv_r2_std', 0):.4f})
"""
# Residual plot
fig, ax = plt.subplots(1, 2, figsize=(12, 5))
# Actual vs Predicted
ax[0].scatter(best_results['y_test'], best_results['y_pred'], alpha=0.5)
ax[0].plot([best_results['y_test'].min(), best_results['y_test'].max()],
[best_results['y_test'].min(), best_results['y_test'].max()], 'r--', lw=2)
ax[0].set_xlabel('Actual')
ax[0].set_ylabel('Predicted')
ax[0].set_title('Actual vs Predicted')
# Residuals histogram
residuals = best_results['y_test'] - best_results['y_pred']
ax[1].hist(residuals, bins=30, edgecolor='black')
ax[1].axvline(x=0, color='r', linestyle='--')
ax[1].set_xlabel('Residuals')
ax[1].set_ylabel('Frequency')
ax[1].set_title('Residuals Distribution')
plt.tight_layout()
plot_fig = fig
else: # Classification
results_summary += f"""
### 📊 Classification Metrics
- **Accuracy:** {best_results['accuracy']:.4f}
- **Cross-Validation Accuracy:** {best_results.get('cv_accuracy_mean', 0):.4f} (±{best_results.get('cv_accuracy_std', 0):.4f})
"""
if 'roc_auc' in best_results:
results_summary += f"- **ROC AUC:** {best_results['roc_auc']:.4f}\n"
# Confusion matrix plot
cm = best_results['confusion_matrix']
fig = go.Figure(data=go.Heatmap(
z=cm,
x=["Predicted 0", "Predicted 1"],
y=["Actual 0", "Actual 1"],
colorscale="Blues",
text=cm,
texttemplate="%{text}",
textfont={"size": 16}
))
fig.update_layout(
title="Confusion Matrix",
xaxis_title="Predicted",
yaxis_title="Actual"
)
plot_fig = fig
# Feature importance if available
if best_results.get('feature_importance'):
fi_df = pd.DataFrame(best_results['feature_importance'],
columns=['Feature', 'Importance'])
fi_df = fi_df.head(10) # Top 10 features
fig2 = px.bar(fi_df, x='Importance', y='Feature',
orientation='h',
title='Top 10 Feature Importances')
plot_fig = fig2
detailed_df = fi_df
# Model comparison table
comparison_df = pd.DataFrame(comparison_data)
results_summary += f"\n### 📈 Top 5 Models Comparison\n{comparison_df.to_markdown(index=False)}"
elif task_type == "Clustering":
# Prepare data for clustering (numeric only)
X = df.select_dtypes(include=[np.number])
if X.empty:
return "No numeric columns found for clustering.", None, None, None
if clustering_algo == "K-Means":
results = run_clustering("K-Means", X, n_clusters=n_clusters)
else: # DBSCAN
results = run_clustering("DBSCAN", X, eps=eps, min_samples=min_samples)
# Add cluster labels to dataframe
df_clustered = df.copy()
df_clustered['Cluster'] = results['labels']
# Generate summary
results_summary = f"## 🎪 {results['method']} Clustering Results\n\n"
results_summary += f"- **Number of clusters found:** {len(set(results['labels'])) - (1 if -1 in results['labels'] else 0)}\n"
if results.get('silhouette_score'):
results_summary += f"- **Silhouette Score:** {results['silhouette_score']:.4f}\n"
if results.get('davies_bouldin_score'):
results_summary += f"- **Davies-Bouldin Index:** {results['davies_bouldin_score']:.4f}\n"
if results.get('calinski_harabasz_score'):
results_summary += f"- **Calinski-Harabasz Index:** {results['calinski_harabasz_score']:.4f}\n"
if results['method'] == "K-Means":
results_summary += f"- **Inertia:** {results['inertia']:.2f}\n"
# Cluster visualization with PCA
try:
pca = PCA(n_components=2)
X_pca = pca.fit_transform(StandardScaler().fit_transform(X))
cluster_df = pd.DataFrame({
'PC1': X_pca[:, 0],
'PC2': X_pca[:, 1],
'Cluster': results['labels']
})
fig = px.scatter(cluster_df, x='PC1', y='PC2', color='Cluster',
title=f'{results["method"]} Clustering (PCA Visualization)',
color_continuous_scale='viridis')
plot_fig = fig
except Exception as e:
print(f"PCA visualization error: {e}")
# Cluster statistics
cluster_stats = df_clustered.groupby('Cluster').agg(['mean', 'std', 'count']).round(2)
detailed_df = cluster_stats
else: # Rule-Based
try:
if rule_algo == "Apriori (Simple)":
# Prepare transaction data
transactions = prepare_transaction_data(df, trans_col if trans_col else None)
if not transactions:
return "No valid transactions found in the data.", None, None
# Run Apriori
frequent_itemsets, rules = run_association_mining(
transactions,
min_support=min_sup,
min_confidence=min_conf,
min_lift=min_lft
)
results_summary = f"## 🔗 Simple Apriori Association Rules\n\n"
else: # Frequency-Based Rules
# Identify categorical columns
cat_cols = df.select_dtypes(include=['object', 'category']).columns.tolist()
if not cat_cols:
# Try to use all columns as categorical
cat_cols = df.columns.tolist()
rules_list = run_frequency_based_rules(
df,
categorical_cols=cat_cols,
min_frequency=min_sup,
min_cooccurrence=min_conf
)
# Convert to rules format similar to Apriori
rules = []
for rule in rules_list:
rules.append({
'antecedents': {rule['antecedent']},
'consequents': {rule['consequent']},
'support': rule['support'],
'confidence': rule['confidence'],
'lift': rule['lift'],
'rule_str': rule['rule']
})
results_summary = f"## 🔗 Frequency-Based Association Rules\n\n"
if not rules:
return "No association rules found with the given parameters.", None, None
results_summary += f"- **Total rules found:** {len(rules)}\n"
results_summary += f"- **Minimum support:** {min_sup}\n"
results_summary += f"- **Minimum confidence:** {min_conf}\n"
results_summary += f"- **Minimum lift:** {min_lft}\n\n"
# Top 10 rules by confidence
top_rules = rules[:10]
results_summary += "### 🏆 Top 10 Rules by Confidence\n\n"
for idx, rule in enumerate(top_rules):
if 'rule_str' in rule:
rule_text = rule['rule_str']
else:
antecedents = ', '.join(list(rule['antecedents']))
consequents = ', '.join(list(rule['consequents']))
rule_text = f"IF {antecedents} THEN {consequents}"
results_summary += f"{idx+1}. **{rule_text}** \n"
results_summary += f" Support: {rule['support']:.3f}, Confidence: {rule['confidence']:.3f}, Lift: {rule['lift']:.3f}\n\n"
# Prepare rules for visualization
plot_data = []
for rule in rules[:20]: # Limit to 20 for plotting
if 'rule_str' in rule:
rule_name = rule['rule_str'][:50] + "..." if len(rule['rule_str']) > 50 else rule['rule_str']
else:
antecedents = ', '.join(list(rule['antecedents']))[:20]
consequents = ', '.join(list(rule['consequents']))[:20]
rule_name = f"{antecedents}→{consequents}"
plot_data.append({
'rule': rule_name,
'support': rule['support'],
'confidence': rule['confidence'],
'lift': rule['lift']
})
if plot_data:
plot_df = pd.DataFrame(plot_data)
fig = px.scatter(plot_df, x='support', y='confidence',
size='lift', color='lift',
hover_name='rule',
title=f'{rule_algo} Association Rules',
labels={'support': 'Support', 'confidence': 'Confidence'})
plot_fig = fig
# Prepare detailed results
detailed_data = []
for rule in rules[:20]:
if 'rule_str' in rule:
rule_str = rule['rule_str']
else:
antecedents = ', '.join(list(rule['antecedents']))
consequents = ', '.join(list(rule['consequents']))
rule_str = f"{antecedents} → {consequents}"
detailed_data.append({
'Rule': rule_str,
'Support': f"{rule['support']:.3f}",
'Confidence': f"{rule['confidence']:.3f}",
'Lift': f"{rule['lift']:.3f}"
})
detailed_df = pd.DataFrame(detailed_data)
except Exception as e:
return f"Error in rule-based mining: {str(e)}", None, None
# Return successful results
return results_summary, plot_fig, detailed_df
except Exception as e:
traceback.print_exc()
return f"Error in model training: {str(e)}", None, None
train_btn.click(
fn=train_model_wrapper,
inputs=[
task_select, model_select, dataset_select, target_col,
scaler_choice, rfecv_opt, hyperparam_tuning,
clustering_method, n_clusters, eps_value, min_samples_value,
rule_method, transaction_col, min_support, min_confidence, min_lift, max_rule_length,
df_state, clean_state
],
outputs=[model_results, model_plot, detailed_results]
)
# Update column lists for model tab
def update_model_columns(df_path, clean_path, profile):
try:
if clean_path:
df = pd.read_csv(clean_path)
elif df_path:
df = pd.read_csv(df_path)
else:
return gr.Dropdown.update(choices=[]), gr.Dropdown.update(choices=[])
cols = list(df.columns)
# For rule-based, also update transaction column
transaction_choices = cols + [None]
return (
gr.Dropdown.update(choices=cols),
gr.Dropdown.update(choices=transaction_choices, value=None)
)
except:
return gr.Dropdown.update(choices=[]), gr.Dropdown.update(choices=[])
# Update when dataset changes
df_state.change(
fn=update_model_columns,
inputs=[df_state, clean_state, profile_state],
outputs=[target_col, transaction_col]
)
clean_state.change(
fn=update_model_columns,
inputs=[df_state, clean_state, profile_state],
outputs=[target_col, transaction_col]
)
# Apply preparation changes
def _apply_all_preparations(df_path, selected_cols, high_card_threshold, high_card_action):
try:
if not df_path:
return "No dataset loaded", None
df = pd.read_csv(df_path)
if selected_cols:
df = df[selected_cols]
high_card_cols = detect_high_cardinality_cols(df, high_card_threshold)
if high_card_action == "Drop columns" and high_card_cols:
cols_to_drop = [col for col, count in high_card_cols]
df = df.drop(columns=cols_to_drop)
tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
df.to_csv(tmp.name, index=False)
summary = f"""
**Preparation Summary:**
- Final dataset: {len(df)} rows, {len(df.columns)} columns
- High cardinality columns found: {len(high_card_cols)}
- Columns kept: {', '.join(df.columns.tolist())}
"""
return summary, tmp.name
except Exception as e:
return f"Preparation failed: {str(e)}", None
apply_all_btn.click(
fn=_apply_all_preparations,
inputs=[df_state, available_cols, high_card_threshold, high_card_action],
outputs=[prep_output, clean_state]
)
# Helper functions for prepare tab
def select_all_callback(df_path):
try:
if not df_path:
return gr.update(choices=[], value=[])
df = pd.read_csv(df_path)
cols = [str(c) for c in df.columns]
return gr.update(choices=cols, value=cols)
except:
return gr.update(choices=[], value=[])
def deselect_all_callback(df_path):
try:
if not df_path:
return gr.update(choices=[], value=[])
df = pd.read_csv(df_path)
cols = [str(c) for c in df.columns]
return gr.update(choices=cols, value=[])
except:
return gr.update(choices=[], value=[])
select_all_cols.click(fn=select_all_callback, inputs=[df_state], outputs=[available_cols])
deselect_all_cols.click(fn=deselect_all_callback, inputs=[df_state], outputs=[available_cols])
# NLP visualization
def _nl_visualize(query, clean_path, df_path):
try:
df = None
if clean_path:
df = pd.read_csv(clean_path)
elif df_path:
df = pd.read_csv(df_path)
if df is None or df.empty:
return None
instr = nlp_to_chart_instruction(query, df)
fig = render_chart_from_instruction(instr, df)
return fig
except Exception as e:
print("nlp visualize error", e)
return None
nl_btn.click(fn=_nl_visualize, inputs=[nl_input, clean_state, df_state], outputs=[nl_plot])
# Report generation
def _generate_report(profile, prep_summary, model_summary, feature_imp_df):
try:
lines = []
lines.append("# 📊 DataSynth Executive Report")
lines.append("---\n")
lines.append("## 📈 Data Profile Summary")
if profile:
lines.append(f"- **Rows:** {profile.get('rows', 0):,}")
lines.append(f"- **Columns:** {profile.get('columns', 0)}")
lines.append(f"- **Numeric Columns:** {len(profile.get('numeric_cols', []))}")
lines.append(f"- **Categorical Columns:** {len(profile.get('categorical_cols', []))}")
else:
lines.append("No profile data available.")
lines.append("\n## 🔧 Data Preparation")
lines.append(prep_summary or "No preparation performed.")
lines.append("\n## 🤖 Model Results")
lines.append(model_summary or "No model training performed.")
if isinstance(feature_imp_df, pd.DataFrame) and not feature_imp_df.empty:
lines.append("\n## 📊 Feature Importance")
lines.append(feature_imp_df.to_markdown(index=False))
# Save markdown
tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".md", mode="w", encoding="utf-8")
tmp.write("\n".join(lines))
tmp.flush()
return tmp.name
except Exception as e:
return None
report_btn.click(
fn=_generate_report,
inputs=[profile_state, prep_output, model_results, detailed_results],
outputs=[report_download]
)
demo.load(lambda: None, outputs=[])
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860, share=False) |