app.py

import pandas as pd
import re
import torch
import gradio as gr
import matplotlib.pyplot as plt
import seaborn as sns
from transformers import pipeline

cached_df = None
cached_file_name = None

# Load sentiment pipeline
sentiment_pipeline = pipeline(
    "text-classification",
    model="pvaluedotone/bigbird-flight-2",
    tokenizer="pvaluedotone/bigbird-flight-2",
    device=0 if torch.cuda.is_available() else -1
)

# Contractions dictionary
contractions_dict = {
    "don't": "do not", "can't": "cannot", "i'm": "i am", "it's": "it is",
    "he's": "he is", "she's": "she is", "they're": "they are", "we're": "we are",
    "you're": "you are", "that's": "that is", "there's": "there is", "what's": "what is",
    "won't": "will not", "isn't": "is not", "aren't": "are not", "wasn't": "was not",
    "weren't": "were not", "didn't": "did not", "doesn't": "does not", "haven't": "have not",
    "hasn't": "has not", "hadn't": "had not", "wouldn't": "would not", "shouldn't": "should not",
    "couldn't": "could not", "mustn't": "must not", "let's": "let us"
}
contractions_pattern = re.compile(r"\b(" + "|".join(re.escape(k) for k in contractions_dict.keys()) + r")\b")

def expand_contractions(text: str) -> str:
    def replace(match):
        return contractions_dict[match.group(0)]
    return contractions_pattern.sub(replace, text)

# Emoticon mapping
emoticon_dict = {
    ":)": "smile", ":-)": "smile", ":(": "sad", ":-(": "sad",
    ";)": "wink", ";-)": "wink", ":d": "laugh", ":-d": "laugh",
    ":p": "playful", ":-p": "playful", ":'(": "cry", ":/": "skeptical",
    ":'-)": "tears_of_joy"
}

def clean_text(text: str) -> str:
    if not isinstance(text, str):
        return ""
    text = re.sub(r"http\S+|@\w+", "", text)
    text = expand_contractions(text)
    try:
        import emoji
        text = emoji.demojize(text)
    except ImportError:
        pass
    for emoticon, desc in emoticon_dict.items():
        text = text.replace(emoticon, f" {desc} ")
    text = re.sub(r"#(\w+)", r"\1", text)
    text = re.sub(r"\s+", " ", text).strip()
    return text

def predict_sentiment(texts):
    results = sentiment_pipeline(texts, truncation=False, batch_size=32)
    sentiments = []
    confidences = []
    for r in results:
        label_num = int(r['label'].split('_')[-1])
        sentiments.append(label_num)
        confidences.append(r['score'])
    return sentiments, confidences

def recategorize(labels, mode, pos_threshold, neg_threshold):
    if mode == "Original (1–10)":
        return labels
    elif mode == "Binary (Positive vs Negative)":
        return ["Positive" if lbl >= pos_threshold else "Negative" for lbl in labels]
    elif mode == "Ternary (Pos/Neu/Neg)":
        return [
            "Positive" if lbl >= pos_threshold else
            "Negative" if lbl <= neg_threshold else
            "Neutral" for lbl in labels
        ]

def analyze_sentiment(file, text_column, mode, pos_thresh, neg_thresh, auto_fix, apply_cleaning):
    global cached_df, cached_file_name

    try:
        df = pd.read_csv(file.name)
    except Exception as e:
        return f"Error reading CSV file: {e}", None, None, None, None, None

    if text_column not in df.columns:
        return "Selected column not found.", None, None, None, None, None

    if (
        cached_df is not None and
        cached_file_name == file.name and
        "sentiment_1to10" in cached_df.columns and
        "confidence" in cached_df.columns
    ):
        df = cached_df.copy()
    else:
        if apply_cleaning:
            df["processed_text"] = df[text_column].apply(clean_text)
        else:
            df["processed_text"] = df[text_column].astype(str)

        predictions, confidences = predict_sentiment(df["processed_text"].tolist())
        df["sentiment_1to10"] = predictions
        df["confidence"] = confidences

        cached_df = df.copy()
        cached_file_name = file.name

    if mode == "Ternary (Pos/Neu/Neg)":
        if pos_thresh <= neg_thresh:
            if auto_fix:
                neg_thresh = pos_thresh - 1
                if neg_thresh < 1:
                    return "⚠️ Unable to auto-correct: thresholds out of valid range (1–10).", None, None, None, None, None
            else:
                return (
                    f"⚠️ Invalid thresholds: Positive min ({pos_thresh}) must be greater than Negative max ({neg_thresh}).",
                    None, None, None, None, None
                )

    df["sentiment_recategorised"] = recategorize(df["sentiment_1to10"], mode, pos_thresh, neg_thresh)

    output_file = "bigbird_sentiment_results.csv"
    df.to_csv(output_file, index=False)

    if "plot1_path" not in globals():
        plt.figure(figsize=(6, 4))
        sns.countplot(x=df["sentiment_1to10"], palette="Blues")
        plt.title("Original 10-Class Sentiment Distribution")
        plt.tight_layout()
        global plot1_path
        plot1_path = "original_dist.png"
        plt.savefig(plot1_path)
        plt.close()

    plt.figure(figsize=(6, 4))
    sns.countplot(x=df["sentiment_recategorised"], palette="Set2")
    plt.title(f"Recategorised Sentiment Distribution ({mode})")
    plt.tight_layout()
    plot2_path = "recategorised_dist.png"
    plt.savefig(plot2_path)
    plt.close()

    if "plot3_path" not in globals():
        plt.figure(figsize=(6, 4))
        sns.histplot(df["confidence"], bins=20, color="skyblue", kde=True)
        plt.title("Confidence Score Distribution")
        plt.xlabel("Confidence")
        plt.tight_layout()
        global plot3_path
        plot3_path = "confidence_dist.png"
        plt.savefig(plot3_path)
        plt.close()

    preview = df[[text_column, "processed_text", "sentiment_1to10", "confidence", "sentiment_recategorised"]].head(10)
    return f"✅ Sentiment analysis complete. Used cache: {cached_file_name == file.name}", preview, output_file, plot1_path, plot2_path, plot3_path

def get_text_columns(file):
    try:
        df = pd.read_csv(file.name, nrows=1)
        text_columns = df.select_dtypes(include='object').columns.tolist()
        if not text_columns:
            return gr.update(choices=[], value=None, label="⚠️ No text columns found!")
        return gr.update(choices=text_columns, value=text_columns[0])
    except Exception:
        return gr.update(choices=[], value=None, label="⚠️ Error reading file")

with gr.Blocks() as app:
    gr.Markdown("## ✈️ Sentiment analysis with Big Bird Flight 2")
    gr.Markdown("**Citation:** Mat Roni, S. (2025). *Sentiment analysis with Big Bird Flight 2 on Gradio* (version 1.0) [software]. https://huggingface.co/spaces/pvaluedotone/bigbird-flight-2 DOI: https://doi.org/10.57967/hf/5780")

    with gr.Row():
        file_input = gr.File(label="Upload CSV", file_types=[".csv"])
        column_dropdown = gr.Dropdown(label="Select Text Column", choices=[], interactive=True)

    file_input.change(get_text_columns, inputs=file_input, outputs=column_dropdown)

    output_mode = gr.Radio(
        label="Sentiment Output Type",
        choices=["Original (1–10)", "Binary (Positive vs Negative)", "Ternary (Pos/Neu/Neg)"],
        value="Original (1–10)",
        interactive=True
    )

    pos_thresh_slider = gr.Slider(3, 10, value=7, step=1, label="Positive min", visible=False)
    neg_thresh_slider = gr.Slider(1, 7, value=4, step=1, label="Negative max", visible=False)
    auto_fix_checkbox = gr.Checkbox(label="Auto-correct thresholds if overlapping?", value=True)
    cleaning_checkbox = gr.Checkbox(label="Apply Text Cleaning", value=True)  # ✅ New toggle

    def toggle_thresholds(mode):
        show_pos = mode != "Original (1–10)"
        show_neg = mode == "Ternary (Pos/Neu/Neg)"
        return (
            gr.update(visible=show_pos),
            gr.update(visible=show_neg)
        )

    output_mode.change(toggle_thresholds, inputs=output_mode, outputs=[pos_thresh_slider, neg_thresh_slider])

    run_button = gr.Button("Process sentiment")

    status = gr.Textbox(label="Status")
    df_output = gr.Dataframe(label="Sample Output (Top 10)")
    file_result = gr.File(label="Download Full Results")
    plot_orig = gr.Image(label="Original Sentiment Distribution")
    plot_recat = gr.Image(label="Recategorised Sentiment Distribution")
    plot_conf = gr.Image(label="Confidence Score Distribution")

    run_button.click(
        analyze_sentiment,
        inputs=[
            file_input, column_dropdown, output_mode,
            pos_thresh_slider, neg_thresh_slider, auto_fix_checkbox,
            cleaning_checkbox  # ✅ New input
        ],
        outputs=[status, df_output, file_result, plot_orig, plot_recat, plot_conf]
    )

app.launch(share=True, debug=True)