adding sql generator app

app.py

@@ -0,0 +1,128 @@
+import gradio as gr
+import torch
+import re
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL = "jinesh90/qwen2.5-coder-sql-generator"
+
+print("Loading model...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL,
+    torch_dtype = torch.float16,
+    device_map = "auto",
+    low_cpu_mem_usage = True,
+)
+model.eval()
+print("Ready!")
+
+def clean_sql(text):
+    text = text.strip()
+    clean = re.sub(r'[^\x00-\x7F].*', '', text).strip()
+    for stop in ["###", "assistant", "\n\n"]:
+        if stop in clean:
+            clean = clean.split(stop)[0].strip()
+    return clean
+
+def build_prompt(question, schema):
+    return f"""You are a SQL expert. Generate the simplest and most direct SQL query.
+Use JOINs only when multiple tables are needed.
+
+### Schema:
+{schema}
+
+### Question:
+{question}
+
+### SQL:"""
+
+def generate(question, schema):
+    if not question or not schema:
+        return "Please provide both a question and schema!"
+    
+    messages = [{"role": "user", "content": build_prompt(question, schema)}]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize = False,
+        add_generation_prompt = True
+    )
+    inputs = tokenizer(
+        text,
+        return_tensors = "pt",
+        truncation = True,
+        max_length = 1024
+    ).to(model.device)
+    
+    stop_tokens = [
+        tokenizer.eos_token_id,
+        tokenizer.convert_tokens_to_ids("<|im_end|>"),
+    ]
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens = 200,
+            do_sample = False,
+            temperature = 0,
+            repetition_penalty = 1.3,
+            eos_token_id = stop_tokens,
+            pad_token_id = tokenizer.eos_token_id,
+        )
+    
+    input_len = inputs["input_ids"].shape[1]
+    raw = tokenizer.decode(outputs[0, input_len:], skip_special_tokens=True)
+    return clean_sql(raw)
+
+# Example schemas for demo
+example_schema = """CREATE TABLE employees (
+    id INTEGER,
+    name VARCHAR,
+    salary REAL,
+    department VARCHAR,
+    age INTEGER
+);"""
+
+with gr.Blocks(title="SQL Query Generator") as demo:
+    gr.Markdown("# 🗄️ SQL Query Generator")
+    gr.Markdown("Fine-tuned Qwen2.5-Coder 7B on Spider dataset | 42% execution accuracy")
+    
+    with gr.Row():
+        with gr.Column():
+            schema = gr.Textbox(
+                label = "Database Schema (CREATE TABLE statements)",
+                value = example_schema,
+                lines = 10
+            )
+            question = gr.Textbox(
+                label = "Question",
+                placeholder = "How many employees have salary > 50000?",
+                lines = 2
+            )
+            btn = gr.Button("🚀 Generate SQL", variant="primary")
+        
+        with gr.Column():
+            output = gr.Code(
+                label = "Generated SQL",
+                language = "sql"
+            )
+            gr.Markdown("""
+            ### 📊 Model Stats
+            - **Base model**: Qwen2.5-Coder-7B
+            - **Training data**: Spider dataset (7.9k samples)
+            - **Simple queries**: 64.2% accuracy
+            - **Complex queries**: 17.0% accuracy
+            - **Overall**: 42% execution accuracy
+            """)
+    
+    btn.click(fn=generate, inputs=[question, schema], outputs=output)
+    
+    gr.Examples(
+        examples=[
+            ["How many employees are there?", example_schema],
+            ["Find all employees with salary greater than 50000", example_schema],
+            ["What is the average salary by department?", example_schema],
+        ],
+        inputs=[question, schema]
+    )
+
+demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+torch
+transformers
+accelerate
+gradio