Update app.py

app.py

@@ -2,48 +2,46 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr  
 import torch  
   
-title = "EZChat"  
-description = "A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT-medium)"  
+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'  
-tokenizer.add_special_tokens({'pad_token': '[EOS]'})  
-tokenizer.pad_token = tokenizer.eos_token  
 
-# Model
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")  
-  
-#predict  
-def predict(input, history=[]):  
-    # tokenize the new input sentence  
-    new_user_input_ids = tokenizer.encode(  
-        input + tokenizer.eos_token, padding=True, truncation=True, return_tensors="pt"  
-    )  
-  
-    # append the new user input tokens to the chat history  
-    bot_input_ids = torch.cat([torch.tensor(history), new_user_input_ids], dim=-1) if history else new_user_input_ids  
-  
-    # generate a response  
-    chat_history_ids = model.generate(  
-        bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id  
-    )  
-  
-    # convert the tokens to text, and then split the responses into lines  
-    response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)  
-      
-    return response, chat_history_ids.tolist()[0]  
-  
-  
-iface = gr.Interface(  
-    fn=predict,  
-    title=title,  
-    description=description,  
-    examples=examples,  
-    inputs=["text", gr.inputs.Slider(0, 4000, default=2000, label='Chat History')],  
-    outputs=["text", "text"],  
-    theme="ParityError/Anime",  
-)  
-  
-iface.launch()  
+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")     
+    
+def predict(input, history=[]):    
+    new_user_input_ids = tokenizer.encode(input, return_tensors="pt")    
+    
+    bot_input_ids = torch.cat([torch.tensor(history), new_user_input_ids], dim=-1) if history else new_user_input_ids    
+    
+    chat_history_ids = model.generate(bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id)    
+    
+    response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)    
+        
+    return response    
+    
+iface = gr.Interface(    
+    fn=predict,    
+    title=title,    
+    description=description,    
+    examples=examples,    
+    inputs="text",    
+    outputs="text",    
+    theme="ParityError/Anime",    
+)    
+    
+iface.launch()