Update app.py

app.py

@@ -1,15 +1,24 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import TapexTokenizer, BartForConditionalGeneration
+import pandas as pd
 
-def chatbot_response(user_message):
-    model_name = "gpt2"  # You can change this to any other model from the list above
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(model_name)
+model_name = "microsoft/tapex-large-sql-execution"  # You can change this to any other model from the list above
+tokenizer = TapexTokenizer.from_pretrained(model_name)
+model = BartForConditionalGeneration.from_pretrained(model_name)
 
-    inputs = tokenizer.encode("User: " + user_message, return_tensors="pt")
-    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+data = {
+    "year": [1896, 1900, 1904, 2004, 2008, 2012],
+    "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
+}
+table = pd.DataFrame.from_dict(data)
 
+def chatbot_response(user_message):
+    
+    inputs = tokenizer.encode("User: " + user_message, return_tensors="pt")
+    encoding = tokenizer(table=table, query=inputs, return_tensors="pt")
+    outputs = model.generate(**encoding)
+    response = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    
     return response
 
 # Define the chatbot interface using Gradio