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app.py

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+import re, torch, gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "kirankotha/mistral7b-sql-model"
+
+tok = AutoTokenizer.from_pretrained(model_id)
+mdl = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+
+if tok.pad_token is None:
+    tok.pad_token = tok.eos_token
+
+def gen_sql(question, context):
+    prompt = f"You are a text-to-SQL model.\n### Input:\n{question}\n### Context:\n{context}\n### Response:\n"
+    ids = tok(prompt, return_tensors="pt").to(mdl.device)
+    with torch.no_grad():
+        out = mdl.generate(
+            **ids,
+            max_new_tokens=128,
+            do_sample=False,
+            pad_token_id=tok.pad_token_id
+        )
+    text = tok.decode(out[0], skip_special_tokens=True)
+    m = re.findall(r"```(.*?)```", text, flags=re.DOTALL)
+    return m[-1].strip() if m else text.strip()
+
+demo = gr.Interface(
+    fn=gen_sql,
+    inputs=["text","text"],
+    outputs="text",
+    title="Mistral-7B Text-to-SQL Demo",
+    description="Fine-tuned Mistral-7B model for text-to-SQL generation.",
+    examples=[["Which product has the highest price?",
+               "CREATE TABLE products (id INTEGER, name TEXT, price REAL)"]]
+)
+
+demo.launch()