Update app.py

app.py

@@ -1,22 +1,58 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-models = {
-    "Mistral-7B": "mistralai/Mistral-7B-Instruct-v0.2",
-    "Falcon-7B": "tiiuae/falcon-7b-instruct"
-}
-
-def ask_model(question, model_choice):
-    client = InferenceClient(models[model_choice])
-    prompt = f"Answer this questionnaire item: {question} (Strongly disagree - Strongly agree)"
-    return client.text_generation(prompt=prompt)
-
-gr.Interface(
-    fn=ask_model,
-    inputs=[
-        gr.Textbox(label="Questionnaire Item"),
-        gr.Dropdown(list(models.keys()), label="Choose Model")
-    ],
-    outputs="text",
-    title="LLM-Powered Questionnaire"
-).launch()
+from transformers import pipeline
+import pandas as pd
+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):
+    questions = [q.strip() for q in questions_text.strip().split("\n") if q.strip()]
+    
+    global response_table
+    response_table = []
+    output_lines = []
+
+    for i, question in enumerate(questions, 1):
+        result = classifier(question, LIKERT_OPTIONS, 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}")
+        output_lines.append("")
+
+        row = {"Item #": i, "Item": question}
+        row.update({label: round(probs.get(label, 0.0), 3) for label in LIKERT_OPTIONS})
+        response_table.append(row)
+
+    return "\n".join(output_lines)
+
+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 interface
+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.")
+
+    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)
+
+    submit_btn.click(fn=classify_likert, inputs=questions_input, outputs=output_box)
+    csv_btn.click(fn=download_csv, inputs=[], outputs=file_output)
+
+demo.launch()