Update app.py

app.py

@@ -1,64 +1,60 @@
 import gradio as gr
-from transformers import pipeline
 import pandas as pd
 import io
+from transformers import pipeline
 
-# Load once
-classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+# Available zero-shot classification models
+models = {
+    "EN: deberta-v3-large-zeroshot": "MoritzLaurer/deberta-v3-large-zeroshot-v2.0",
+    "MULTI: mDeBERTa-v3-xnli": "MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7",
+    "MULTI: xlm-roberta-xnli": "joeddav/xlm-roberta-large-xnli"
+}
 
 response_table = []
 
-def classify_items(questions_text, labels_text):
+def classify_items(questions_text, labels_text, model_choice):
+    labels = [l.strip() for l in labels_text.split(",") if l.strip()]
     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.", ""
+        return "Please enter both questionnaire items and response labels.", None
 
+    classifier = pipeline("zero-shot-classification", model=models[model_choice])
     global response_table
     response_table = []
     output_lines = []
 
     for i, question in enumerate(questions, 1):
         result = classifier(question, labels, multi_label=False)
-        probs = dict(zip(result['labels'], result['scores']))
-
+        row = {"Item #": i, "Item": question}
         output_lines.append(f"{i}. {question}")
-        for label in labels:
-            output_lines.append(f"→ {label}: {round(probs.get(label, 0.0), 3)}")
+        for label, score in zip(result["labels"], result["scores"]):
+            row[label] = round(score, 3)
+            output_lines.append(f"→ {label}: {round(score, 3)}")
         output_lines.append("")
-
-        row = {"Item #": i, "Item": question}
-        row.update({label: round(probs.get(label, 0.0), 3) for label in labels})
         response_table.append(row)
 
     return "\n".join(output_lines), None
 
 def download_csv():
-    global response_table
-    if not response_table:
-        return None
     df = pd.DataFrame(response_table)
-    csv_buffer = io.StringIO()
-    df.to_csv(csv_buffer, index=False)
-    return csv_buffer.getvalue()
+    buffer = io.StringIO()
+    df.to_csv(buffer, index=False)
+    return buffer.getvalue()
 
-# Gradio UI
+# Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🧠 Zero-Shot Classification for Questionnaire Responses")
-    gr.Markdown("Paste questionnaire items (one per line), and provide your own response labels (comma-separated).")
-
-    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_items, inputs=[questions_input, labels_input], outputs=[output_box, file_output])
-    csv_btn.click(fn=download_csv, inputs=[], outputs=file_output)
+    gr.Markdown("## 🧠 Zero-Shot Classification with Model Selection")
+    gr.Markdown("Students can enter multiple questionnaire items and define their own response labels. The selected model will classify each item and provide probabilities.")
+
+    model_dropdown = gr.Dropdown(choices=list(models.keys()), label="Choose a model")
+    labels_input = gr.Textbox(label="Response Options (comma-separated)", placeholder="e.g., Strongly disagree, Disagree, Neutral, Agree, Strongly agree")
+    questions_input = gr.Textbox(label="Questionnaire Items (one per line)", lines=10)
+    output_box = gr.Textbox(label="Model Output", lines=20)
+    submit_btn = gr.Button("Classify")
+    download_btn = gr.Button("📥 Download CSV")
+    file_output = gr.File(label="Download CSV", visible=False)
+
+    submit_btn.click(fn=classify_items, inputs=[questions_input, labels_input, model_dropdown], outputs=[output_box, file_output])
+    download_btn.click(fn=download_csv, inputs=[], outputs=file_output)
 
 demo.launch()