Update app.py

app.py

@@ -1,7 +1,32 @@
 import gradio as gr
-from transformers import pipeline, Conversation
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, BlenderbotForConditionalGeneration, BlenderbotForCausalLM, BlenderbotTokenizer
+
+tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
+model = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill",add_cross_attention=False)
+
+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.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]).replace("<s>","").split("</s>")
+    response = [(response[i], response[i+1]) for i in range(0, len(response), 2)]  # convert to tuples of list
+    return response, history
+
+gr.Interface(
+		fn = predict, 
+		inputs = ["textbox","state"], 
+		outputs = ["chatbot","state"],
+		theme ="seafoam",
+		title = title, 
+		description = description, 
+		article = article 
+		).launch(enable_queue=True)
 
-pipeline = pipeline(task="conversational", model="facebook/blenderbot-400M-distill")
-con1 = Conversation("Going to the movies tonight - any suggestions?")
-con2 = Conversation("What's the last book you have read?")
-converse([con1, con2])