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| import gradio as gr | |
| import pandas as pd | |
| import json | |
| import io | |
| def run_e2e_pipeline(input_file): | |
| if input_file is None: | |
| return "Error: No file uploaded. Please upload a CSV file and try again.", [] | |
| try: | |
| try: | |
| df = pd.read_csv(input_file) | |
| except Exception: | |
| content = input_file.read() | |
| df = pd.read_csv(io.StringIO(content.decode("utf-8"))) | |
| except Exception as e: | |
| return f"Error reading CSV: {e}", [] | |
| import platform | |
| import sys | |
| import datetime | |
| try: | |
| import torch | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| except ImportError: | |
| device = "cpu" | |
| num_rows = len(df) | |
| num_cols = len(df.columns) | |
| missing = df.isnull().sum().sum() | |
| numeric_df = df.select_dtypes(include='number') | |
| anomalies = (numeric_df < 0).sum().sum() if not numeric_df.empty else 0 | |
| # Per-column missing values and data types | |
| col_missing = df.isnull().sum().to_dict() | |
| col_types = df.dtypes.apply(lambda x: str(x)).to_dict() | |
| total_cells = num_rows * num_cols | |
| filled_cells = total_cells - missing | |
| quality_score = int(round((filled_cells / total_cells) * 100)) if total_cells > 0 else 0 | |
| report = { | |
| "Status": "APPROVED" if missing == 0 else "REVIEW", | |
| "Rows": int(num_rows), | |
| "Columns": int(num_cols), | |
| "Missing Values": int(missing), | |
| "Anomalies": int(anomalies), | |
| "Quality Score": quality_score, | |
| "Per Column Missing Values": col_missing, | |
| "Column Data Types": col_types, | |
| "Run Metadata": { | |
| "timestamp": datetime.datetime.now().isoformat(), | |
| "python_version": sys.version, | |
| "platform": platform.platform(), | |
| "device": device | |
| } | |
| } | |
| report_path = "validation_report.json" | |
| with open(report_path, "w") as f: | |
| json.dump(report, f, indent=2) | |
| # Determine cause if data has missing values or anomalies, and specify columns with missing values | |
| cause = [] | |
| # Only check and report missing value columns once | |
| missing_cols = [(col, val) for col, val in col_missing.items() if val > 0] | |
| if missing > 0: | |
| missing_details = ', '.join([f"{col} ({val})" for col, val in missing_cols]) | |
| cause.append(f"missing values present ({missing}) in columns: {missing_details}") | |
| if anomalies > 0: | |
| cause.append(f"anomalies present ({anomalies})") | |
| cause_str = f"\nCause: {', '.join(cause)}." if cause else "\nData is clean." | |
| summary = ( | |
| f"Validation Complete\nStatus: {report['Status']}\nQuality Score: {report['Quality Score']}%\n" | |
| f"Rows: {num_rows}\nColumns: {num_cols}\nMissing Values: {missing}\nAnomalies: {anomalies}\nReport: {report_path}\n" | |
| f"Timestamp: {report['Run Metadata']['timestamp']}\n" | |
| f"Checked Perform: Data loaded, missing values checked, anomalies checked, per-column missing values and types checked, device and environment info captured." + cause_str | |
| ) | |
| return summary, [report_path] | |
| # --- Spotify-inspired, colorful, emoji-rich UI --- | |
| with gr.Blocks() as demo: | |
| gr.Markdown(""" | |
| <div id='pro-header' style='display:flex;align-items:center;justify-content:center;background:linear-gradient(90deg,#0f2027 0%,#2c5364 60%,#f5ba57 100%);padding:26px 0 16px 0;border-radius:0 0 40px 40px;box-shadow:0 6px 32px #2c536488;'> | |
| <img src='https://img.icons8.com/color/96/verified-account--v1.png' style='height:70px;margin-right:22px;filter:drop-shadow(0 0 12px #f5ba57);'> | |
| <div style='text-align:left;'> | |
| <h1 style='font-size:2.7em;margin:0;color:#fff;letter-spacing:2.5px;font-family:Montserrat,sans-serif;text-shadow:0 2px 12px #0f202799;'> | |
| <span style='font-size:1.2em;'>📊</span> <span style='color:#f5ba57;'>Data Quality Gatekeeper</span> <span style='font-size:1.2em;'>🛡️</span> | |
| </h1> | |
| <div style='font-size:1.2em;color:#fff;opacity:0.96;text-shadow:0 2px 8px #0f202777;'> | |
| <b>Upload your CSV, run the pipeline, and download insights!</b> <span style='color:#f5ba57;'>Professional. Private. Powerful.</span> | |
| </div> | |
| </div> | |
| </div> | |
| """) | |
| gr.Markdown(""" | |
| <div style='display:flex;justify-content:center;gap:22px;margin:22px 0 0 0;'> | |
| <div style='background:linear-gradient(135deg,#2c5364 0%,#0f2027 100%);border-radius:22px;padding:22px 28px;box-shadow:0 4px 24px #2c536488;min-width:340px;max-width:420px;'> | |
| <ul style='list-style:none;padding:0;margin:0;font-size:1.2em;'> | |
| <li style='margin-bottom:14px;'> | |
| <span style='font-size:1.5em;'>📂</span> <b style='color:#f5ba57;text-shadow:0 2px 8px #fff;'>Upload</b> <code style='background:#fffbe7;border-radius:6px;padding:2px 8px;'>sample_data.csv</code> | |
| <img src='https://img.icons8.com/fluency/32/upload.png' style='vertical-align:middle;margin-left:6px;'> | |
| </li> | |
| <li style='margin-bottom:14px;'> | |
| <span style='font-size:1.5em;'>⚡</span> <b style='color:#f5ba57;text-shadow:0 2px 8px #fff;'>Run</b> the pipeline | |
| <img src='https://img.icons8.com/fluency/32/play.png' style='vertical-align:middle;margin-left:6px;'> | |
| </li> | |
| <li> | |
| <span style='font-size:1.5em;'>📥</span> <b style='color:#f5ba57;text-shadow:0 2px 8px #fff;'>Download</b> results | |
| <img src='https://img.icons8.com/fluency/32/download.png' style='vertical-align:middle;margin-left:6px;'> | |
| </li> | |
| </ul> | |
| </div> | |
| </div> | |
| """) | |
| with gr.Row(equal_height=True): | |
| with gr.Column(scale=1): | |
| file_input = gr.File(label="Step 1: Upload your CSV data file", file_types=[".csv"], elem_id="file-upload", min_width=0) | |
| with gr.Column(scale=2): | |
| btn = gr.Button("Step 2: Run Data Validation Pipeline", elem_id="run-btn", min_width=0) | |
| with gr.Row(): | |
| output_console = gr.Textbox(label="Step 3: Validation Results & Insights", lines=8, elem_id="output-console") | |
| with gr.Row(): | |
| output_files = gr.Files(label="Step 4: Download Validation Report", elem_id="output-files") | |
| btn.click(run_e2e_pipeline, file_input, [output_console, output_files]) | |
| gr.Markdown(""" | |
| <div id='pro-footer' style='text-align:center;margin:0;padding:14px 0 0 0;'> | |
| <span style='font-size:1.5em;'>🏅</span> <b style='color:#f5ba57;font-family:Montserrat,sans-serif;text-shadow:0 2px 8px #0f202799;'>Phase1 Local AI Solution</b> — <span style='color:#fff;text-shadow:0 2px 8px #0f202777;'>Built with Gradio</span> <span style='font-size:1.5em;'>🚀</span> | |
| </div> | |
| """) | |
| if __name__ == "__main__": | |
| demo.launch(css=""" | |
| .gradio-container {padding: 0 !important;} | |
| body { | |
| background: linear-gradient(135deg, #0f2027 0%, #2c5364 60%, #f5ba57 100%) !important; | |
| min-height: 100vh; | |
| font-family: 'Montserrat', 'Segoe UI', Arial, sans-serif; | |
| animation: bgmove 18s ease-in-out infinite alternate; | |
| } | |
| @keyframes bgmove { | |
| 0% {background-position: 0% 50%;} | |
| 100% {background-position: 100% 50%;} | |
| } | |
| #pro-header { | |
| margin-bottom: 0; | |
| box-shadow: 0 6px 32px #f5ba5788; | |
| } | |
| .gradio-container { | |
| background: transparent !important; | |
| box-shadow: none !important; | |
| padding: 0 !important; | |
| } | |
| #output-console textarea { | |
| background: linear-gradient(90deg,#fffbe7 0%,#f5ba57 100%); | |
| color: #2c5364; | |
| font-family: 'Fira Mono', 'Consolas', monospace; | |
| font-size: 1.1em; | |
| border-radius: 12px; | |
| border: 2.5px solid #f5ba57; | |
| box-shadow: 0 2px 12px #2c536488; | |
| } | |
| #run-btn button { | |
| background: linear-gradient(90deg, #f5ba57 0%, #2c5364 100%); | |
| color: #0f2027; | |
| font-size: 1.2em; | |
| border-radius: 10px; | |
| border: 2.5px solid #f5ba57; | |
| box-shadow: 0 2px 12px #f5ba5744; | |
| transition: background 0.2s, color 0.2s; | |
| font-weight: bold; | |
| text-shadow: 0 2px 8px #fffbe7cc; | |
| } | |
| #run-btn button:hover { | |
| background: linear-gradient(90deg, #2c5364 0%, #f5ba57 100%); | |
| color: #fffbe7; | |
| } | |
| #file-upload input[type="file"] { | |
| background: linear-gradient(90deg,#fffbe7 0%,#f5ba57 100%); | |
| color: #2c5364; | |
| border-radius: 10px; | |
| border: 2.5px solid #f5ba57; | |
| padding: 10px; | |
| font-size: 1.1em; | |
| font-weight: bold; | |
| box-shadow: 0 2px 12px #2c536488; | |
| } | |
| #pro-footer { | |
| color: #fff; | |
| background: transparent; | |
| margin-top: 0; | |
| font-size: 1.2em; | |
| text-shadow: 0 2px 12px #0f202788; | |
| } | |
| #output-files .gr-file-list { | |
| background: linear-gradient(90deg,#fffbe7 0%,#f5ba57 100%); | |
| border-radius: 12px; | |
| border: 2.5px solid #f5ba57; | |
| color: #2c5364; | |
| font-size: 1.1em; | |
| box-shadow: 0 2px 12px #2c536488; | |
| } | |
| """) |