Update app.py

app.py

@@ -1,31 +1,24 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-from datasets import load_dataset
 
 # Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("hrshtsharma2012/NL2SQL-Picard-final")
-model = AutoModelForSeq2SeqLM.from_pretrained("hrshtsharma2012/NL2SQL-Picard-final")
-
-# Load a part of the Spider dataset
-spider_dataset = load_dataset("spider", split='train[:5]')
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
+model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
 
 def generate_sql(query):
-    inputs = tokenizer(query, return_tensors="pt", padding=True)
+    input_text = "translate English to SQL: " + query
+    inputs = tokenizer(input_text, return_tensors="pt", padding=True)
     outputs = model.generate(**inputs, max_length=512)
     sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return sql_query
 
-# Use examples from the Spider dataset
-example_questions = [(question['question'],) for question in spider_dataset]
-
 # Create a Gradio interface
 interface = gr.Interface(
     fn=generate_sql,
     inputs=gr.Textbox(lines=2, placeholder="Enter your natural language query here..."),
     outputs="text",
-    examples=example_questions,
-    title="NL to SQL with Picard",
-    description="This model converts natural language queries into SQL using the Spider dataset. Try one of the example questions or enter your own!"
+    title="NL to SQL with T5",
+    description="This model converts natural language queries into SQL. Enter your query!"
 )
 
 # Launch the app