updated question structure

README.md

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

database.py

@@ -0,0 +1,67 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model and tokenizer
+model_name = "EleutherAI/gpt-neo-2.7B"  # Replace with a suitable model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Example schema
+schema = {
+    "products": {
+        "columns": ["product_id", "name", "price", "category_id"],
+        "relations": "category_id -> categories.id",
+    },
+    "categories": {
+        "columns": ["id", "category_name"],
+        "relations": None,
+    },
+    "orders": {
+        "columns": ["order_id", "customer_name", "product_id", "order_date"],
+        "relations": "product_id -> products.product_id",
+    },
+}
+
+def generate_context(schema):
+    """
+    Generate context dynamically from the schema.
+    """
+    context = "### Database Schema ###\n\n"
+    for table, details in schema.items():
+        context += f"Table: {table}\nColumns: {', '.join(details['columns'])}\n"
+        if details.get("relations"):
+            context += f"Relations: {details['relations']}\n"
+        context += "\n"
+
+    context += "### Instructions ###\n"
+    context += (
+        "Generate SQL queries based on the user's question. "
+        "Understand the schema to identify relevant tables and relationships. "
+        "If the question involves multiple tables, use appropriate joins.\n"
+    )
+    return context
+
+# Generate dynamic context
+context = generate_context(schema)
+
+def answer_question(context, question):
+    """
+    Generate an SQL query or database-related response using the model.
+    """
+    prompt = f"{context}\n\nUser Question: {question}\nSQL Query or Answer:"
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
+    outputs = model.generate(inputs.input_ids, max_length=256, num_beams=5, early_stopping=True)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Interactive loop
+print("Database Assistant is ready. Ask your questions!")
+
+# Example interactive questions
+questions = [
+    "describe the product table for me, what kind of data it is storing and all"
+]
+
+for user_question in questions:
+    print(f"Question: {user_question}")
+    response = answer_question(context, user_question)
+    print("\nGenerated Response:\n", response, "\n")
+