Update app.py

app.py

@@ -6,31 +6,33 @@ import io
 # Load once
 classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 
-LIKERT_OPTIONS = ["Strongly disagree", "Disagree", "Neutral", "Agree", "Strongly agree"]
-
 response_table = []
 
-def classify_likert(questions_text):
+def classify_items(questions_text, labels_text):
     questions = [q.strip() for q in questions_text.strip().split("\n") if q.strip()]
+    labels = [l.strip() for l in labels_text.strip().split(",") if l.strip()]
     
+    if not labels or not questions:
+        return "Please provide both items and at least two response options.", ""
+
     global response_table
     response_table = []
     output_lines = []
 
     for i, question in enumerate(questions, 1):
-        result = classifier(question, LIKERT_OPTIONS, multi_label=False)
+        result = classifier(question, labels, multi_label=False)
         probs = dict(zip(result['labels'], result['scores']))
+
         output_lines.append(f"{i}. {question}")
-        for label in LIKERT_OPTIONS:
-            prob = round(probs.get(label, 0.0), 3)
-            output_lines.append(f"→ {label}: {prob}")
+        for label in labels:
+            output_lines.append(f"→ {label}: {round(probs.get(label, 0.0), 3)}")
         output_lines.append("")
 
         row = {"Item #": i, "Item": question}
-        row.update({label: round(probs.get(label, 0.0), 3) for label in LIKERT_OPTIONS})
+        row.update({label: round(probs.get(label, 0.0), 3) for label in labels})
         response_table.append(row)
 
-    return "\n".join(output_lines)
+    return "\n".join(output_lines), None
 
 def download_csv():
     global response_table
@@ -41,18 +43,22 @@ def download_csv():
     df.to_csv(csv_buffer, index=False)
     return csv_buffer.getvalue()
 
-# Gradio interface
+# Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("# Likert-Style Zero-Shot Classifier")
-    gr.Markdown("Paste questionnaire items. Each will be classified into: Strongly disagree → Strongly agree, with probabilities.")
+    gr.Markdown("# 🧠 Zero-Shot Classification for Questionnaire Responses")
+    gr.Markdown("Paste questionnaire items (one per line), and provide your own response labels (comma-separated).")
 
-    questions_input = gr.Textbox(label="Enter multiple items (one per line)", lines=10, placeholder="e.g.\nI feel in control of my life.\nI enjoy being around others...")
-    output_box = gr.Textbox(label="Classification Output", lines=20)
-    submit_btn = gr.Button("Classify Items")
-    csv_btn = gr.Button("📥 Download CSV")
-    file_output = gr.File(label="Download CSV", visible=False)
+    with gr.Row():
+        with gr.Column():
+            questions_input = gr.Textbox(label="Questionnaire Items", lines=10, placeholder="e.g.\nI feel in control of my life.\nI enjoy being around others.")
+            labels_input = gr.Textbox(label="Response Options (comma-separated)", placeholder="Strongly disagree, Disagree, Neutral, Agree, Strongly agree")
+            submit_btn = gr.Button("Classify Items")
+            csv_btn = gr.Button("📥 Download CSV")
+        with gr.Column():
+            output_box = gr.Textbox(label="Classification Output", lines=20)
+            file_output = gr.File(label="Download CSV", visible=False)
 
-    submit_btn.click(fn=classify_likert, inputs=questions_input, outputs=output_box)
+    submit_btn.click(fn=classify_items, inputs=[questions_input, labels_input], outputs=[output_box, file_output])
     csv_btn.click(fn=download_csv, inputs=[], outputs=file_output)
 
 demo.launch()