Space with UI

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Path to model on the Hugging Face Hub
+MODEL_NAME = "Yuk050/gemma-3-1b-text-to-sql-model"
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    torch_dtype=torch.float32,  # safer default for CPU Spaces
+    device_map="auto",
+)
+
+def generate_sql(schema: str, natural_language_query: str) -> str:
+    user_message = f"Given the following database schema:\n\n{schema}\n\nGenerate the SQL query for: {natural_language_query}"
+    
+    messages = [{"role": "user", "content": user_message}]
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt"
+    ).to(model.device)
+
+    outputs = model.generate(
+        input_ids,
+        max_new_tokens=256,
+        do_sample=True,
+        temperature=0.7,
+        top_k=50,
+        top_p=0.95,
+        eos_token_id=tokenizer.eos_token_id
+    )
+
+    response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
+    return response.strip()
+
+iface = gr.Interface(
+    fn=generate_sql,
+    inputs=[
+        gr.Textbox(lines=10, label="Database Schema", placeholder="e.g., CREATE TABLE Employees (...)"),
+        gr.Textbox(lines=2, label="Natural Language Query", placeholder="e.g., Select all employees with salary > 50000")
+    ],
+    outputs=gr.Textbox(lines=5, label="Generated SQL Query"),
+    title="Text-to-SQL with Gemma 3 1B",
+    description="Enter a database schema and natural language question. The model will generate the SQL."
+)
+
+iface.launch()

requirements.txt

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+transformers
+torch
+gradio
+accelerate
+bitsandbytes
+unsloth