Update app.py

app.py

@@ -6,7 +6,8 @@ from datasets import load_dataset
 tokenizer = T5Tokenizer.from_pretrained('t5-small', legacy=False)
 model = T5ForConditionalGeneration.from_pretrained('t5-small')
 
-dataset = load_dataset("b-mc2/sql-create-context")
+# dataset = load_dataset("b-mc2/sql-create-context")
+dataset = load_dataset("rotten_tomatoes", split="train")
 
 # examples = []
 
@@ -17,8 +18,8 @@ dataset = load_dataset("b-mc2/sql-create-context")
 
 def generate_sql(question):
     # Format the question for the model if needed. For example:
-    input_text = f"translate English to SQL: {question}"
-    # input_text = f"{question}"  # Directly use the question if the model is fine-tuned for SQL generation
+    # input_text = f"translate English to SQL: {question}"
+    input_text = f"{question}"  # Directly use the question if the model is fine-tuned for SQL generation
     
     # Tokenize the input text
     input_ids = tokenizer.encode(input_text, return_tensors="pt")