Update app.py

app.py

@@ -1,23 +1,35 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from datasets import load_dataset
 
-# Load the NL2SQL model
-model_id = "hrshtsharma2012/NL2SQL-Picard-final"
-nl2sql = pipeline("text2text-generation", model=model_id)
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("hrshtsharma2012/NL2SQL-Picard-final")
+model = AutoModelForSeq2SeqLM.from_pretrained("hrshtsharma2012/NL2SQL-Picard-final")
 
-def generate_sql(nl_query):
-    # Generate the SQL query from the natural language input
-    sql_query = nl2sql(nl_query)[0]["generated_text"]
+# Initialize the pipeline
+nl2sql_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
+
+# Load a part of the Spider dataset
+spider_dataset = load_dataset("spider", split='train[:5]')
+
+def generate_sql(query):
+    results = nl2sql_pipeline(query)
+    sql_query = results[0]['generated_text']
     return sql_query
 
-# Create the Gradio interface
+# 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(label="Natural Language Query"),
-    outputs=gr.Textbox(label="SQL Query"),
-    title="NL to SQL Converter",
-    description="Enter a natural language query and get the corresponding SQL query.",
+    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!"
 )
 
+# Launch the app
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch()