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| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| import os | |
| import zipfile | |
| # ---------- EDA FUNCTION ---------- | |
| def run_eda(file): | |
| df = pd.read_csv(file.name) | |
| # Basic validation | |
| if "text" not in df.columns or "player" not in df.columns: | |
| return "CSV must contain 'player' and 'text' columns.", None | |
| # Drop missing text | |
| df = df.dropna(subset=["text"]) | |
| # Create output directory | |
| os.makedirs("outputs", exist_ok=True) | |
| # ---------- Dataset Summary ---------- | |
| summary = [] | |
| summary.append(f"Number of rows: {df.shape[0]}") | |
| summary.append(f"Number of columns: {df.shape[1]}") | |
| summary.append("\nMissing values:") | |
| summary.append(str(df.isnull().sum())) | |
| # ---------- Text Length Analysis ---------- | |
| df["text_length"] = df["text"].str.len() | |
| stats = df["text_length"].describe() | |
| summary.append("\nText Length Statistics:") | |
| summary.append(str(stats)) | |
| # Save summary | |
| summary_path = "outputs/eda_summary.txt" | |
| with open(summary_path, "w") as f: | |
| f.write("\n".join(summary)) | |
| # ---------- Plot: Text Length Distribution ---------- | |
| plt.figure() | |
| plt.hist(df["text_length"], bins=50) | |
| plt.xlabel("Text Length (characters)") | |
| plt.ylabel("Frequency") | |
| plt.title("Distribution of Reddit Comment Lengths") | |
| text_len_plot = "outputs/text_length_distribution.png" | |
| plt.savefig(text_len_plot) | |
| plt.close() | |
| # ---------- Plot: Top Players ---------- | |
| plt.figure() | |
| df["player"].value_counts().head(10).plot(kind="bar") | |
| plt.xlabel("Player") | |
| plt.ylabel("Number of Mentions") | |
| plt.title("Top 10 Most Mentioned NFL Players") | |
| player_plot = "outputs/top_players.png" | |
| plt.savefig(player_plot) | |
| plt.close() | |
| # ---------- Save Cleaned Dataset ---------- | |
| cleaned_csv = "outputs/cleaned_nfl_data.csv" | |
| df.to_csv(cleaned_csv, index=False) | |
| # ---------- Zip Results ---------- | |
| zip_path = "outputs/nfl_eda_results.zip" | |
| with zipfile.ZipFile(zip_path, "w") as zipf: | |
| zipf.write(summary_path) | |
| zipf.write(text_len_plot) | |
| zipf.write(player_plot) | |
| zipf.write(cleaned_csv) | |
| return ( | |
| "\n".join(summary), | |
| zip_path | |
| ) | |
| # ---------- GRADIO UI ---------- | |
| interface = gr.Interface( | |
| fn=run_eda, | |
| inputs=gr.File(label="Upload NFL Reddit CSV"), | |
| outputs=[ | |
| gr.Textbox(label="EDA Summary", lines=20), | |
| gr.File(label="Download All Results (ZIP)") | |
| ], | |
| title="NFL Reddit EDA & NLP Lab Analyzer", | |
| description=( | |
| "Upload an NFL Reddit CSV file to perform exploratory data analysis. " | |
| "This application generates dataset insights, visualizations, and " | |
| "a downloadable ZIP with results and plots." | |
| ) | |
| ) | |
| interface.launch() | |