Update app.py

app.py

@@ -1,60 +1,22 @@
 import gradio as gr
-import pandas as pd
-import io
-from transformers import pipeline
+from huggingface_hub import InferenceClient
 
-# 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"
+    "Mistral-7B": "mistralai/Mistral-7B-Instruct-v0.2",
+    "Falcon-7B": "tiiuae/falcon-7b-instruct"
 }
 
-response_table = []
-
-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()]
-    if not labels or not questions:
-        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)
-        row = {"Item #": i, "Item": question}
-        output_lines.append(f"{i}. {question}")
-        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("")
-        response_table.append(row)
-
-    return "\n".join(output_lines), None
-
-def download_csv():
-    df = pd.DataFrame(response_table)
-    buffer = io.StringIO()
-    df.to_csv(buffer, index=False)
-    return buffer.getvalue()
-
-# Gradio interface
-with gr.Blocks() as demo:
-    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()
+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()