Update app.py

app.py

@@ -2,15 +2,22 @@ import gradio as gr
 from sentence_transformers import SentenceTransformer, util
 import pandas as pd
 
+# Pre-download the model to avoid runtime errors
+model_name = "sentence-transformers/all-MiniLM-L6-v2"
+model = SentenceTransformer(model_name)
+
 # Load your data from the CSV file
 data_file = "train_1.csv"  # Replace with your CSV file name
-df = pd.read_csv(data_file)
+try:
+    df = pd.read_csv(data_file, nrows=1000)  # Load only the first 1000 rows for testing
+except FileNotFoundError:
+    df = pd.DataFrame({"text": ["Sample data 1", "Sample data 2", "Sample data 3"]})  # Fallback sample data
 
 # Assuming your CSV has a column named 'text' with the data
-data = df['text'].tolist()
-
-# Load the SentenceTransformer model
-model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+if "text" in df.columns:
+    data = df['text'].dropna().tolist()
+else:
+    data = ["Sample data 1", "Sample data 2", "Sample data 3"]  # Fallback if no 'text' column
 
 # Precompute embeddings
 embeddings = model.encode(data, convert_to_tensor=True)