Create app.py

app.py

@@ -0,0 +1,25 @@
+import gradio as gr
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load model and tokenizer from local files
+model_path = "./"
+tokenizer = T5Tokenizer.from_pretrained(model_path)
+model = T5ForConditionalGeneration.from_pretrained(model_path)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+model.eval()
+
+def generate_sql(user_input):
+    inputs = tokenizer(user_input, return_tensors="pt", truncation=True, padding=True).to(device)
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_length=512)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+gr.Interface(
+    fn=generate_sql,
+    inputs=gr.Textbox(lines=15, placeholder="Paste your full SQL prompt here..."),
+    outputs=gr.Textbox(label="Generated SQL Query"),
+    title="HISAB AI – Text2SQL Generator",
+    description="Paste your schema + user query + instructions. Model returns a PostgreSQL query."
+).launch()