Create app.py

app.py

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+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 `pvaluedotone/bigbird-flight`")
+    gr.Markdown("**Citation:** Mat Roni, S. (2025). *Sentiment analysis with Big Bird Flight on Gradio* (version 1.0) [software]. https://huggingface.co/spaces/pvaluedotone/bigbird-flight")
+
+    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)