updated question structure

README.md

@@ -5,7 +5,7 @@ colorFrom: red
 colorTo: red
 sdk: gradio
 sdk_version: 5.11.0
-app_file: gpt_neo_db.py
+app_file: app.py
 pinned: false
 python: 3.9
 ---

app.py

@@ -9,41 +9,9 @@ db_schema = {
     "customers": ["customer_id", "name", "email", "phone_number"]
 }
 
-# Load the model and tokenizer
-model_name = "EleutherAI/gpt-neox-20b"  # You can also use "Llama-2-7b" or another model
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
 
-def generate_sql_query(context, question):
-    """
-    Generate an SQL query based on the question and context.
-
-    Args:
-        context (str): Description of the database schema or table relationships.
-        question (str): User's natural language query.
-
-    Returns:
-        str: Generated SQL query.
-    """
-    # Prepare the prompt
-    prompt = f"""
-    Context: {context}
-
-    Question: {question}
-
-    Write an SQL query to address the question based on the context.
-    Query:
-    """
-    # Tokenize input
-    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024).to("cuda" if torch.cuda.is_available() else "cpu")
-
-    # Generate SQL query
-    output = model.generate(inputs.input_ids, max_length=512, num_beams=5, early_stopping=True)
-    query = tokenizer.decode(output[0], skip_special_tokens=True)
-
-    # Extract query from the output
-    sql_query = query.split("Query:")[-1].strip()
-    return sql_query
+def dummy_function(schema_description, user_question):
+    print(user_question)
 
 # Schema as a context for the model
 schema_description = json.dumps(db_schema, indent=4)
@@ -57,5 +25,5 @@ while True:
         break
 
     # Generate SQL query
-    sql_query = generate_sql_query(schema_description, user_question)
+    sql_query = dummy_function(schema_description, user_question)
     print(f"Generated SQL Query:\n{sql_query}\n")