Update app.py

app.py

@@ -1,11 +1,44 @@
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import TapexTokenizer, BartForConditionalGeneration
+import pandas as pd
+import torch
+
+import numpy as np
+import time
+import os
+#import pkg_resources
+
+'''
+# Get a list of installed packages and their versions
+installed_packages = {pkg.key: pkg.version for pkg in pkg_resources.working_set}
 
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+# Print the list of packages
+for package, version in installed_packages.items():
+    print(f"{package}=={version}")
+'''
 
+# Load the chatbot model
+chatbot_model_name = "microsoft/DialoGPT-medium" 
+tokenizer = AutoTokenizer.from_pretrained(chatbot_model_name)
+model = AutoModelForCausalLM.from_pretrained(chatbot_model_name)
+
+# Load the SQL Model
+model_name = "microsoft/tapex-large-finetuned-wtq"
+sql_tokenizer = TapexTokenizer.from_pretrained(model_name)
+sql_model = BartForConditionalGeneration.from_pretrained(model_name)
+
+data = {
+    "year": [1896, 1900, 1904, 2004, 2008, 2012],
+    "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
+}
+table = pd.DataFrame.from_dict(data)
 
 def predict(input, history=[]):
+
+    # Check if the user input is a question
+    is_question = "?" in user_message
+    
     # tokenize the new input sentence
     new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
 
@@ -29,6 +62,8 @@ interface = gr.Interface(
     css=".footer {display:none !important}",
     inputs=["text", "state"],
     outputs=["chatbot", "state"],
+    title="ST Chatbot",
+    description="Type your message in the box above, and the chatbot will respond.",
 )
 
 if __name__ == '__main__':