Update app.py

app.py

@@ -1,28 +1,28 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-def chatbot_response(user_message):
-    model_name = "gpt2"  # You can change this to any other model from the list above
+def execute_sql(user_query):
+    model_name = "microsoft/tapex-large-sql-execution"  # Tapex large SQL execution model
     tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-    inputs = tokenizer.encode("User: " + user_message, return_tensors="pt")
-    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
+    inputs = tokenizer(user_query, return_tensors="pt", padding=True)
+    outputs = model.generate(inputs['input_ids'], attention_mask=inputs['attention_mask'], max_length=1024)
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
     return response
 
 # Define the chatbot interface using Gradio
 iface = gr.Interface(
-    fn=chatbot_response,
-    inputs=gr.Textbox(prompt="You:"),
+    fn=execute_sql,
+    inputs=gr.Textbox(prompt="Enter your SQL query:"),
     outputs=gr.Textbox(),
     live=True,
     capture_session=True,
-    title="Chatbot",
-    description="Type your message in the box above, and the chatbot will respond.",
+    title="SQL Execution Chatbot",
+    description="Type your SQL query in the box above, and the chatbot will execute it.",
 )
 
 # Launch the Gradio interface
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()