Update app.py

app.py

@@ -1,41 +1,56 @@
 import gradio as gr
 import pandas as pd
 import random
+from transformers import DebertaV2Tokenizer, DebertaV2Model
 
-# 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
+# Importing and setting up a DeBERTa v2 model (for demonstration)
+tokenizer = DebertaV2Tokenizer.from_pretrained('microsoft/deberta-v2-xlarge')
+model = DebertaV2Model.from_pretrained('microsoft/deberta-v2-xlarge')
+
+# Hardcoded sample data
+data = {
+    "QueryID": [
+        "Tastemade _16_46", "MyChart _23_23", "USPS MOBILE _20_10", 
+        "The Washington Post Classic _21_20", "QuickBooks Accounting: Invoicing & Expenses _9_40"
+    ],
+    "Segment": [
+        "Some common applications are to target adverti...",
+        "The security of your information and data whil...",
+        "If you still have concerns about cookies, you ...",
+        "cookies help us and third parties understand ...",
+        "Under certain conditions, more fully described..."
+    ]
+}
+
+df = pd.DataFrame(data)
+
+# Fake predictions for demonstration
 fake_predictions = {
-    101: 'Positive Response',
-    102: 'Negative Response',
-    103: 'Positive Response',
-    104: 'Negative Response',
-    105: 'Positive Response'
+    "Tastemade _16_46": "Irrelevant",
+    "MyChart _23_23": "Irrelevant",
+    "USPS MOBILE _20_10": "Irrelevant",
+    "The Washington Post Classic _21_20": "Irrelevant",
+    "QuickBooks Accounting: Invoicing & Expenses _9_40": "Irrelevant",
+    # ... Add more mappings if needed
 }
 
+def preprocess_data(segment):
+    # Sample preprocessing steps (not actually applied in fake prediction)
+    tokenized_input = tokenizer(segment, return_tensors="pt", padding='max_length', truncation=True, max_length=512)
+    # Normally, you would pass this through the model, but here we're just simulating
+    return tokenized_input
+
 def predict(query_id):
     # Simulate a model prediction
+    segment = df[df['QueryID'] == query_id]['Segment'].iloc[0]
+    processed_data = preprocess_data(segment)  # Preprocessing (for show)
     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)]
+    inputs=gr.inputs.Dropdown(list(df['QueryID'].unique()), label="Select QueryID"),
+    outputs="text"
 )
 
 iface.launch()