Update app.py

app.py

@@ -7,31 +7,31 @@ import sqlparse
 # Load model and tokenizer
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model = AutoModelForCausalLM.from_pretrained(
-    "onkolahmet/text_to_sql", 
+    "onkolahmet/Qwen2-0.5B-Instruct-SQL-generator", 
     torch_dtype="auto",
     device_map="auto"
 )
-tokenizer = AutoTokenizer.from_pretrained("onkolahmet/text_to_sql")
+tokenizer = AutoTokenizer.from_pretrained("onkolahmet/Qwen2-0.5B-Instruct-SQL-generator")
 
-# Few-shot examples to include in each prompt
-examples = [
-    {
-        "question": "Get the names and emails of customers who placed an order in the last 30 days.",
-        "sql": "SELECT name, email FROM customers WHERE order_date >= DATE_SUB(CURDATE(), INTERVAL 30 DAY);"
-    },
-    {
-        "question": "Find all employees with a salary greater than 50000.",
-        "sql": "SELECT * FROM employees WHERE salary > 50000;"
-    },
-    {
-        "question": "List all product names and their categories where the price is below 50.",
-        "sql": "SELECT name, category FROM products WHERE price < 50;"
-    },
-    {
-        "question": "How many users registered in the year 2022?",
-        "sql": "SELECT COUNT(*) FROM users WHERE YEAR(registration_date) = 2022;"
-    }
-]
+# # Few-shot examples to include in each prompt
+# examples = [
+#     {
+#         "question": "Get the names and emails of customers who placed an order in the last 30 days.",
+#         "sql": "SELECT name, email FROM customers WHERE order_date >= DATE_SUB(CURDATE(), INTERVAL 30 DAY);"
+#     },
+#     {
+#         "question": "Find all employees with a salary greater than 50000.",
+#         "sql": "SELECT * FROM employees WHERE salary > 50000;"
+#     },
+#     {
+#         "question": "List all product names and their categories where the price is below 50.",
+#         "sql": "SELECT name, category FROM products WHERE price < 50;"
+#     },
+#     {
+#         "question": "How many users registered in the year 2022?",
+#         "sql": "SELECT COUNT(*) FROM users WHERE YEAR(registration_date) = 2022;"
+#     }
+# ]
 
 def generate_sql(question, context=None):
     # Construct prompt with few-shot examples and context if available
@@ -41,9 +41,9 @@ def generate_sql(question, context=None):
     if context and context.strip():
         prompt += f"Table Context:\n{context}\n\n"
     
-    # Add few-shot examples
-    for ex in examples:
-        prompt += f"Q: {ex['question']}\nSQL: {ex['sql']}\n\n"
+    # # Add few-shot examples
+    # for ex in examples:
+    #     prompt += f"Q: {ex['question']}\nSQL: {ex['sql']}\n\n"
     
     # Add the current question
     prompt += f"Q: {question}\nSQL:"
@@ -297,7 +297,7 @@ with gr.Blocks(title="Text to SQL Converter") as demo:
     ### About
     This app uses a fine-tuned language model to convert natural language questions into SQL queries.
     
-    - **Model**: [onkolahmet/text_to_sql](https://huggingface.co/onkolahmet/text_to_sql)
+    - **Model**: [onkolahmet/Qwen2-0.5B-Instruct-SQL-generator](https://huggingface.co/onkolahmet/Qwen2-0.5B-Instruct-SQL-generator)
     - **How to use**: 
       1. Enter your question in natural language
       2. If you have specific table schemas, add them in the Table Context field