Update app.py

app.py

@@ -7,19 +7,19 @@ 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("rotten_tomatoes", split="train")
+dataset = load_dataset("wikisql", split="train")
 
-# examples = []
+examples = []
 
-# for i in range(3):  # Let's take the first 3 examples
-#     item = dataset[i]
-#     question = item['question']
-#     examples.append([question])
+for i in range(3):  # Let's take the first 3 examples
+    item = dataset[i]
+    question = item['question']
+    examples.append([question])
 
 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")
@@ -39,7 +39,7 @@ iface = gr.Interface(
     outputs=gr.Textbox(),
     title="Natural Language to SQL",
     description="This app uses a Seq2Seq model to generate SQL queries from natural language questions.",
-    # examples=examples
+    examples=examples
 )
 
 # Launch the app