Update app.py

app.py

@@ -2,7 +2,7 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from transformers import TapexTokenizer, BartForConditionalGeneration
 import pandas as pd
-import torch
+import gradio as gr
 
 import numpy as np
 import time
@@ -42,47 +42,41 @@ def predict(input, history=[]):
     # Check if the user input is a question
     is_question = "?" in input
 
+    '''
     if is_question: 
         sql_encoding = sql_tokenizer(table=table, query=input + sql_tokenizer.eos_token, return_tensors="pt")
-        #sql_outputs = sql_model.generate(**sql_encoding)
-        #sql_response = sql_tokenizer.batch_decode(sql_outputs, skip_special_tokens=True)
-
-        # append the new user input tokens to the chat history
-        bot_input_ids = torch.cat([torch.LongTensor(history), sql_encoding], dim=-1)
-          
-        # generate a response
-        history = sql_model.generate(bot_input_ids, max_length=1000, pad_token_id=sql_tokenizer.eos_token_id).tolist()
-    
-        # convert the tokens to text, and then split the responses into the right format
-        response = sql_tokenizer.decode(history[0]).split("<|endoftext|>")
-        response = [(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)]  # convert to tuples of list
-        
-        '''
-        bot_input_ids = torch.cat([torch.LongTensor(history), sql_encoding], dim=-1)
-        history = sql_model.generate(bot_input_ids, max_length=1000, pad_token_id=sql_tokenizer.eos_token_id).tolist()
-        response = sql_tokenizer.decode(history[0]).split("<|endoftext|>")
-        response = [(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)]
-        '''
+        sql_outputs = sql_model.generate(**sql_encoding)
+        sql_response = sql_tokenizer.batch_decode(sql_outputs, skip_special_tokens=True)
+
     else:
-        # tokenize the new input sentence
-        new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
-        
-        # append the new user input tokens to the chat history
-        bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-          
-        # generate a response
-        history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+    '''
+    
+    # tokenize the new input sentence
+    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
+    
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+      
+    # generate a response
+    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+
+    # convert the tokens to text, and then split the responses into the right format
+    response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    response = [(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)]  # convert to tuples of list
     
-        # convert the tokens to text, and then split the responses into the right format
-        response = tokenizer.decode(history[0]).split("<|endoftext|>")
-        response = [(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)]  # convert to tuples of list
-        
     return response, history
 
 
-import gradio as gr
+def sqlquery(input):
+
+    sql_encoding = sql_tokenizer(table=table, query=input + sql_tokenizer.eos_token, return_tensors="pt")
+    sql_outputs = sql_model.generate(**sql_encoding)
+    sql_response = sql_tokenizer.batch_decode(sql_outputs, skip_special_tokens=True)
 
-interface = gr.Interface(
+    return sql_response
+
+
+chat_interface = gr.Interface(
     fn=predict,
     theme="default",
     css=".footer {display:none !important}",
@@ -92,5 +86,24 @@ interface = gr.Interface(
     description="Type your message in the box above, and the chatbot will respond.",
 )
 
+sql_interface = gr.Interface(
+    fn=sqlquery,
+    theme="default",
+    inputs=gr.Textbox(prompt="You:"),
+    outputs=gr.Textbox(),
+    live=True,
+    capture_session=True,
+    title="ST SQL Chat",
+    description="Type your message in the box above, and the chatbot will respond.",
+)
+
+combine_interface = gr.TabbedInterface(
+    interface_list=[
+        chat_interface,
+        sql_interface
+    ],
+    tab_names=['Chatbot' ,'SQL Chat'],
+)
+
 if __name__ == '__main__':
-    interface.launch()
+    combine_interface.launch()