Update app.py

app.py

@@ -6,6 +6,9 @@ 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",
@@ -21,7 +24,7 @@ def clean_text(text):
     return text.lower().strip()
 
 def predict_sentiment(texts):
-    results = sentiment_pipeline(texts, truncation=True, batch_size=32)
+    results = sentiment_pipeline(texts, truncation=False, batch_size=32)
     sentiments = []
     confidences = []
     for r in results:
@@ -41,8 +44,9 @@ def recategorize(labels, mode, pos_threshold, neg_threshold):
             "Negative" if lbl <= neg_threshold else
             "Neutral" for lbl in labels
         ]
+def analyze_sentiment(file, text_column, mode, pos_thresh, neg_thresh, auto_fix):
+    global cached_df, cached_file_name
 
-def analyze_sentiment(file, text_column, mode, pos_thresh, neg_thresh):
     try:
         df = pd.read_csv(file.name)
     except Exception as e:
@@ -51,24 +55,54 @@ def analyze_sentiment(file, text_column, mode, pos_thresh, neg_thresh):
     if text_column not in df.columns:
         return "Selected column not found.", None, None, None, None, None
 
-    df["clean_text"] = df[text_column].apply(clean_text)
-    predictions, confidences = predict_sentiment(df["clean_text"].tolist())
-    df["sentiment_1to10"] = predictions
-    df["confidence"] = confidences
+    # Check if sentiment analysis already done and file is unchanged
+    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:
+        # Clean and predict
+        df["clean_text"] = df[text_column].apply(clean_text)
+        predictions, confidences = predict_sentiment(df["clean_text"].tolist())
+        df["sentiment_1to10"] = predictions
+        df["confidence"] = confidences
+        # Cache result
+        cached_df = df.copy()
+        cached_file_name = file.name
+
+    # 🛑 Check thresholds
+    if mode == "Ternary (Pos/Neu/Neg)":
+        if pos_thresh <= neg_thresh:
+            if auto_fix:
+                neg_thresh = pos_thresh - 1
+                if neg_thresh < 1:
+                    return "⚠️ Cannot 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
+                )
+
+    # Apply recategorization
     df["sentiment_recategorised"] = recategorize(df["sentiment_1to10"], mode, pos_thresh, neg_thresh)
 
     # Save results
     output_file = "bigbird_sentiment_results.csv"
     df.to_csv(output_file, index=False)
 
-    # Plot 1: Original 10-class sentiment distribution
-    plt.figure(figsize=(6, 4))
-    sns.countplot(x=df["sentiment_1to10"], palette="Blues")
-    plt.title("Original 10-Class Sentiment Distribution")
-    plt.tight_layout()
-    plot1_path = "original_dist.png"
-    plt.savefig(plot1_path)
-    plt.close()
+    # Plot 1: Original 10-class sentiment distribution (only if new analysis)
+    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()
 
     # Plot 2: Recategorized sentiment distribution
     plt.figure(figsize=(6, 4))
@@ -79,19 +113,22 @@ def analyze_sentiment(file, text_column, mode, pos_thresh, neg_thresh):
     plt.savefig(plot2_path)
     plt.close()
 
-    # Plot 3: Confidence score distribution
-    plt.figure(figsize=(6, 4))
-    sns.histplot(df["confidence"], bins=20, color="orange", kde=True)
-    plt.title("Confidence Score Distribution")
-    plt.xlabel("Confidence")
-    plt.tight_layout()
-    plot3_path = "confidence_dist.png"
-    plt.savefig(plot3_path)
-    plt.close()
+    # Plot 3: Confidence score distribution (only if new analysis)
+    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()
 
     # Sample preview
     preview = df[[text_column, "sentiment_1to10", "confidence", "sentiment_recategorised"]].head(10)
-    return f"Sentiment analysis complete. Processed {len(df)} rows.", preview, output_file, plot1_path, plot2_path, plot3_path
+    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:
@@ -106,7 +143,7 @@ def get_text_columns(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")
-    gr.Markdown("Upload a CSV, choose a text column, select output style (10-class, binary, or ternary), and analyze.")
+    gr.Markdown("Upload a CSV, choose a text column to analyse, select output style (10-class, binary, or ternary), and analyse.")
 
     with gr.Row():
         file_input = gr.File(label="Upload CSV", file_types=[".csv"])
@@ -121,8 +158,10 @@ with gr.Blocks() as app:
         interactive=True
     )
 
-    pos_thresh_slider = gr.Slider(5, 10, value=7, step=1, label="Positive Threshold", visible=False)
-    neg_thresh_slider = gr.Slider(1, 5, value=4, step=1, label="Negative Threshold", visible=False)
+    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)
+
 
     def toggle_thresholds(mode):
         show_pos = mode != "Original (1–10)"
@@ -134,19 +173,19 @@ with gr.Blocks() as app:
 
     output_mode.change(toggle_thresholds, inputs=output_mode, outputs=[pos_thresh_slider, neg_thresh_slider])
 
-    run_button = gr.Button("Run Sentiment Analysis")
+    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="Recategorized 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],
+        inputs=[file_input, column_dropdown, output_mode, pos_thresh_slider, neg_thresh_slider, auto_fix_checkbox],
         outputs=[status, df_output, file_result, plot_orig, plot_recat, plot_conf]
     )
 
-app.launch(debug=True)
+app.launch(share=True, debug=True)