Create app.py

app.py

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+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import gradio as gr
+
+tokenizer = T5Tokenizer.from_pretrained('t5-small')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = T5ForConditionalGeneration.from_pretrained('cssupport/t5-small-awesome-text-to-sql')
+model = model.to(device)
+model.eval()
+
+def generate_sql(input_prompt):
+    inputs = tokenizer(input_prompt, padding=True, truncation=True, return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_length=512)
+    generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_sql
+
+def gradio_interface(tables, query):
+    input_prompt = f"tables:\n{tables}\nquery for:{query}"
+    return generate_sql(input_prompt)
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Textbox(lines=5, label="Context Tables", placeholder="EXAMPLE: CREATE TABLE student_course_attendance (student_id VARCHAR); CREATE TABLE students (student_id VARCHAR)"),
+        gr.Textbox(lines=2, label="Query for", placeholder="EXAMPLE: List the id of students who never attends courses?")
+    ],
+    outputs=gr.Textbox(label="Generated SQL Query", placeholder="EXAMPLE OUTPUT: The generated SQL query is: SELECT student_id FROM students WHERE NOT student_id IN (SELECT student_id FROM student_course_attendance)"),
+    title="Text to SQL Generator"
+)
+
+iface.launch()