Update app.py

app.py

@@ -1,33 +1,26 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import logging
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
 
 # Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("hrshtsharma2012/NL2SQL-Picard-final")
-model = AutoModelForSeq2SeqLM.from_pretrained("hrshtsharma2012/NL2SQL-Picard-final")
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
+model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
 
 def generate_sql(query):
-    try:
-        inputs = tokenizer(query, 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
-    except Exception as e:
-        logging.error(f"Error generating SQL query: {e}")
-        return "Error generating SQL query"
+    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
 
 # Create a Gradio interface
 interface = gr.Interface(
     fn=generate_sql,
     inputs=gr.Textbox(lines=2, placeholder="Enter your natural language query here..."),
     outputs="text",
-    title="NL to SQL with Picard",
+    title="NL to SQL with T5",
     description="This model converts natural language queries into SQL. Enter your query!"
 )
 
 # Launch the app
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch()