Update app.py

app.py

@@ -1,49 +1,38 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer  
-import gradio as gr  
-import torch  
-  
-title = "👋🏻Welcome to Tonic's EZ Chat🚀"  
-description = "You can use this Space to test out the current model (DialoGPT-medium) or duplicate this Space and use it for anyother model on 🤗HuggingFace."  
-examples = [["How are you?"]]  
-  
-# Set the padding token to be used and initialize the model  
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-tokenizer.padding_side = 'left'  
-
 from transformers import AutoModelForCausalLM, AutoTokenizer    
 import gradio as gr    
 import torch    
-    
-title = "👋🏻Welcome to Tonic's EZ Chat🚀"    
-description = "You can use this Space to test out the current model (DialoGPT-medium) or duplicate this Space and use it for any other model on 🤗HuggingFace. Join me on [Discord](https://discord.gg/fpEPNZGsbt) to build together."    
-examples = [["How are you?"]]    
-    
+  
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")  
 tokenizer.padding_side = 'left'    
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")  
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")   
   
-class ChatBot:  
-    def __init__(self):  
-        self.history = []  
+class ChatBot:    
+    def __init__(self):    
+        self.history = []    
   
-    def predict(self, input):  
-        new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")  
-        bot_input_ids = torch.cat([torch.tensor(self.history), new_user_input_ids], dim=-1) if self.history else new_user_input_ids  
-        chat_history_ids = model.generate(bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id)  
-        self.history.append(chat_history_ids[:, bot_input_ids.shape[-1]:].tolist())  
-        response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)  
+    def predict(self, input):        
+        new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")        
+        flat_history = [item for sublist in self.history for item in sublist]      
+        bot_input_ids = torch.cat([torch.tensor(flat_history), new_user_input_ids], dim=-1) if self.history else new_user_input_ids        
+        chat_history_ids = model.generate(bot_input_ids, max_length=2000, pad_token_id=tokenizer.eos_token_id)        
+        self.history.append(chat_history_ids[:, bot_input_ids.shape[-1]:].tolist()[0])        
+        response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)        
         return response  
   
-bot = ChatBot()  
+bot = ChatBot()    
+  
+title = "👋🏻Welcome to Tonic's EZ Chat🚀"    
+description = "You can use this Space to test out the current model (DialoGPT-medium) or duplicate this Space and use it for any other model on 🤗HuggingFace."    
+examples = [["How are you?"]]    
   
-iface = gr.Interface(  
-    fn=bot.predict,  
-    title=title,  
-    description=description,  
-    examples=examples,  
-    inputs="text",  
-    outputs="text",  
-    theme="ParityError/Anime",  
-)  
+iface = gr.Interface(    
+    fn=bot.predict,    
+    title=title,    
+    description=description,    
+    examples=examples,    
+    inputs="text",    
+    outputs="text", 
+    theme="ParityError/Anime"
+)    
   
-iface.launch()
+iface.launch()