Update app.py

app.py

@@ -1,47 +1,45 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
 import gradio as gr
-import torch
-
 
-title = "🤖AI ChatBot"
-description = "Building open-domain chatbots is a challenging area for machine learning research."
-examples = [["How are you?"]]
+title = "Have Fun With ChubbyBot"
+description = """
+<p>
+<center>
+The bot is trained on blended_skill_talk dataset using facebook/blenderbot-400M-distill.
+<img src="https://huggingface.co/spaces/EXFINITE/BlenderBot-UI/resolve/main/img/cover.png" alt="rick" width="250"/>
+</center>
+</p>
+"""
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.06616' target='_blank'>Recipes for building an open-domain chatbot</a></p><p style='text-align: center'><a href='https://parl.ai/projects/recipes/' target='_blank'>Original PARLAI Code</a></p></center></p>"
 
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, BlenderbotForConditionalGeneration, BlenderbotForCausalLM, BlenderbotTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill", padding_side='left)
-model = AutoModelForCausalLM.from_pretrained("facebook/blenderbot-400M-distill")
-
+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"
-    )
+    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=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
+    # 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,
-    title=title,
-    description=description,
-    examples=examples,
-    inputs=["text", "state"],
-    outputs=["chatbot", "state"],
-    theme="finlaymacklon/boxy_violet",
-).launch()
+		fn = predict, 
+		inputs = ["textbox","state"], 
+		outputs = ["chatbot","state"],
+		theme ="seafoam",
+		title = title, 
+		description = description, 
+		article = article 
+		).launch(enable_queue=True)
+