Update app.py

app.py

@@ -1,51 +1,46 @@
-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)"
-examples = [["How are you?"]]
-
-# Set the padding token to be used and initialize the model
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium", padding_side='left')
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
-tokenizer.add_special_tokens({'pad_token': '[EOS]'})
-tokenizer.pad_token = tokenizer.eos_token
-
-#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"
-    )
-    #Attention Mask For Reliable Results
-    attention_mask = inputs['attention_mask']
-
-    # 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=4000, pad_token_id=tokenizer.eos_token_id
-    ).tolist()
-
-    # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    # print('decoded_response-->>'+str(response))
-    response = [
-        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
-    ]  # convert to tuples of list
-    # print('response-->>'+str(response))
-    return response, history
-
-
-gr.Interface(
-    fn=predict,
-    title=title,
-    description=description,
-    examples=examples,
-    inputs=["text", "state"],
-    outputs=["chatbot", "state"],
-    theme="ParityError/Anime",
-).launch()
+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)"  
+examples = [["How are you?"]]  
+  
+# Set the padding token to be used and initialize the model  
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")  
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")  
+tokenizer.add_special_tokens({'pad_token': '[EOS]'})  
+tokenizer.pad_token = tokenizer.eos_token  
+  
+#predict  
+def predict(input, history=[]):  
+    # 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.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()