Update app.py

app.py

@@ -1,64 +1,43 @@
 import gradio as gr
 from transformers import pipeline
 import pandas as pd
-import io
 
-# Load once
-classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+MODEL_MAP = {
+    "MoritzLaurer/deberta-v3-large-zeroshot-v2.0": "MoritzLaurer/deberta-v3-large-zeroshot-v2.0",
+    "MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7": "MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7",
+    "joeddav/xlm-roberta-large-xnli": "joeddav/xlm-roberta-large-xnli"
+}
 
-response_table = []
+def classify_items(model_name, items_text, labels_text):
+    classifier = pipeline("zero-shot-classification", model=MODEL_MAP[model_name])
+    items = [item.strip() for item in items_text.split("\n") if item.strip()]
+    labels = [label.strip() for label in labels_text.split(",") if label.strip()]
 
-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.", ""
+    results = []
+    for item in items:
+        out = classifier(item, labels, multi_label=True)
+        scores = {label: prob for label, prob in zip(out["labels"], out["scores"])}
+        scores["item"] = item
+        results.append(scores)
 
-    global response_table
-    response_table = []
-    output_lines = []
+    df = pd.DataFrame(results).fillna(0)
+    return df, gr.File.update(value=df.to_csv(index=False), visible=True)
 
-    for i, question in enumerate(questions, 1):
-        result = classifier(question, labels, multi_label=False)
-        probs = dict(zip(result['labels'], result['scores']))
-
-        output_lines.append(f"{i}. {question}")
-        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 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()
-
-# Gradio UI
 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).")
+    gr.Markdown("## 🧠 Zero-Shot Questionnaire Classifier")
 
     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)
+        model_choice = gr.Dropdown(choices=list(MODEL_MAP.keys()), label="Choose a zero-shot model")
+
+    item_input = gr.Textbox(label="Enter questionnaire items (one per line)", lines=6, placeholder="I enjoy social gatherings.\nI prefer planning over spontaneity.")
+    label_input = gr.Textbox(label="Enter response options (comma-separated)", placeholder="Strongly disagree, Disagree, Neutral, Agree, Strongly agree")
+
+    run_button = gr.Button("Classify")
+    output_table = gr.Dataframe(label="Classification Results")
+    download_csv = 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)
+    run_button.click(fn=classify_items,
+                     inputs=[model_choice, item_input, label_input],
+                     outputs=[output_table, download_csv])
 
 demo.launch()