Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import random
+
+# Sample data: Replace this with your legal QA dataset structure
+# Assuming columns: 'DocID', 'QueryID', 'Query', 'Segment', 'Label'
+sample_data = pd.DataFrame({
+    'DocID': ['Doc1', 'Doc2', 'Doc3', 'Doc4', 'Doc5'],
+    'QueryID': [101, 102, 103, 104, 105],
+    'Query': ['What is the law regarding...', 'How is the case...', 'Definition of legal term...', 'Procedure for filing...', 'Rights of an individual...'],
+    'Segment': ['Segment1', 'Segment2', 'Segment3', 'Segment4', 'Segment5'],
+    'Label': [1, 0, 1, 0, 1]  # Sample labels
+})
+
+# Fake predictions: You should replace these with actual predictions from your test set
+fake_predictions = {
+    101: 'Positive Response',
+    102: 'Negative Response',
+    103: 'Positive Response',
+    104: 'Negative Response',
+    105: 'Positive Response'
+}
+
+def predict(query_id):
+    # Simulate a model prediction
+    response = fake_predictions.get(query_id, "Unknown QueryID")
+    return response
+
+def get_random_row():
+    # Get a random row from the dataset for demonstration
+    random_row = sample_data.sample().iloc[0]
+    return f"DocID: {random_row['DocID']}, QueryID: {random_row['QueryID']}, Query: {random_row['Query']}, Segment: {random_row['Segment']}"
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Dropdown(list(sample_data['QueryID']), label="Select QueryID"),
+    outputs="text",
+    examples=[get_random_row() for _ in range(5)]
+)
+
+iface.launch()